Cell communication analysis
General Setup
Setup chunk
Setup reticulate
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")[1] "/nas/groups/treutlein/USERS/tomasgomes/projects/liver_regen"Load libraries
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)[1] FALSEuse_python(Sys.which("python"))
py_config()Warning: Python 2 reached EOL on January 1, 2020. Python 2 compatability be removed in an upcoming reticulate release.python: /usr/bin/python
libpython: /usr/lib64/python2.7/config/libpython2.7.so
pythonhome: //usr://usr
version: 2.7.5 (default, May 30 2023, 03:38:55) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)]
numpy: [NOT FOUND]
NOTE: Python version was forced by use_python functionLoad and preprocess data
Load data (from all cells)
library(Seurat)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObjectlibrary(ggplot2)
library(ggridges)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(igraph)
Attaching package: ‘igraph’
The following objects are masked from ‘package:dplyr’:
as_data_frame, groups, union
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
unionlibrary(data.table)data.table 1.14.6 using 72 threads (see ?getDTthreads). Latest news: r-datatable.com
Attaching package: ‘data.table’
The following objects are masked from ‘package:dplyr’:
between, first, lastPrepare a global object with all necessary metadata
allcells_css = readRDS(file = "data/processed/allcells_css.RDS")
end_cells = readRDS(file = "results/endothelial/only_end_cells_zon.RDS")
hep_cells = readRDS(file = "results/zonation_cond/hep_cells_zonation_rank.RDS")
imm_cells = readRDS(file = "results/immune/all_imm_cells.RDS")
# mesenchymal annotation
mes_annot = read.csv("./data/processed/mesenchymal_fresh_annot.csv",
row.names = 1, header = T, )Subset and process each condition
# endothelial
endpop_df = data.frame(row.names = colnames(end_cells),
"subpops" = end_cells@meta.data$endo_simp)
# immune
immpop_df = data.frame(row.names = colnames(imm_cells),
"subpops" = imm_cells@meta.data$immune_annot)
# hepatocyte
heppop_df = lapply(hep_cells, function(x) cbind(colnames(x), as.character(x$zonation_int)))
heppop_df = Reduce(rbind, heppop_df)
heppop_df = data.frame(row.names = heppop_df[,1],
subpops = factor(heppop_df[,2]))
levels(heppop_df$subpops) = c("(-0.00099,0.333]" = "Hepatocytes_Z1",
"(0.333,0.667]" = "Hepatocytes_Z2",
"(0.667,1]" = "Hepatocytes_Z3")
heppop_df$subpops = as.character(heppop_df$subpops)
# mesenchymal
mes_df = mes_annot[,"annotation", drop = F]
colnames(mes_df) = "subpops"
mes_df$subpops[grepl("doublets",mes_df$subpops)] = "Doublets"
subpop_df = rbind(endpop_df, immpop_df, heppop_df, mes_df)
subpop_df$subpops[subpop_df$subpops=="Cycling cells"] = "Dividing endothelial cells"
# add subpop metadata
allcells_css = AddMetaData(allcells_css, subpop_df)
allcells_css$subpops[is.na(allcells_css$subpops)] = allcells_css$allcells_major[is.na(allcells_css$subpops)]
allcells_css$subpops[allcells_css$subpops=="Hepatocyte-Monocyte interaction"] = "Doublets"
# the cells in this object named "Hepatocytes", "Endothelial cells", and "Doublets" have to be removed
## the first two are cells that didn't pass the hep and end analysis
sub_allcells_css = allcells_css[,!(allcells_css$subpops %in% c("Hepatocytes", "Endothelial cells",
"Doublets"))]
# add simplified column - merge some populations, don't include cycling pops and stressed T cells
sub_allcells_css$subpops_simp = sub_allcells_css$subpops
sub_allcells_css$subpops_simp[grepl("MAIT", sub_allcells_css$subpops_simp)] = "MAIT cells"
sub_allcells_css$subpops_simp[grepl("NK cells ", sub_allcells_css$subpops_simp)] = "NK cells"
sub_allcells_css$subpops_simp[grepl("LSEC (high MT", sub_allcells_css$subpops_simp,
fixed = T)] = "LSEC (high MT)"
sub_allcells_css$subpops_simp[grepl("CD8 ab-T ", sub_allcells_css$subpops_simp,
fixed = T)] = "CD8 ab-T cells"
simp_allcells_css = sub_allcells_css[,!(sub_allcells_css$subpops_simp %in% c("ab-T cells (stress)", "Dividing cDCs", "Dividing endothelial cells","Dividing T/NK cells"))]Running CellPhoneDB
Commands for runnin CellPhoneDB will be written here. Results are
output to the local1 disk to avoid I/O issues, but the
output folders should then be copied to:
results/cell_comm_healthy/CellPhoneDB_runs.
NOTE: for some reason I’m getting a SegFault when trying to run the
heatmap_plot command. This was run on the Euler server instead.
#cpdb_path = "results/cell_comm/CellPhoneDB_V3"
cpdb_path = "/local1/scratch/tomasgomes/CellPhoneDB_V3"
cond_cells = list()
for(cond in unique(sub_allcells_css@meta.data$Condition)){
if(!dir.exists(paste0(cpdb_path, "/", cond, "/"))){
dir.create(paste0(cpdb_path, "/", cond, "/"))
}
# subset condition
cond_cells[[cond]] = sub_allcells_css[,sub_allcells_css@meta.data$Condition==cond]
# define metadata
meta = data.frame(row.names = rownames(cond_cells[[cond]]@meta.data),
"Cell" = rownames(cond_cells[[cond]]@meta.data),
"cell_type" = as.character(cond_cells[[cond]]@meta.data$subpops),
stringsAsFactors = F)
write.table(meta, file = paste0(cpdb_path, "/", cond, "/", cond, "_meta_names.txt"),
sep = "\t", col.names = T, row.names = F, quote = F)
# normalise and save
cond_cells[[cond]] = suppressWarnings(SCTransform(cond_cells[[cond]], do.correct.umi = T,
vars.to.regress=c("unique_name", "nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F, verbose = F,
variable.features.n = NULL))
dat = cbind(rownames(cond_cells[[cond]]@assays$SCT@data),
Matrix::as.matrix(cond_cells[[cond]]@assays$SCT@data))
colnames(dat)[1] = "Gene"
write.table(dat, file = paste0(cpdb_path, "/", cond, "/", cond, "_exp_norm.txt"),
sep = "\t", col.names = T, row.names = F, quote = F)
if(!dir.exists(paste0(cpdb_path, "/", cond, "_simp/"))){
dir.create(paste0(cpdb_path, "/", cond, "_simp/"))
}
# subset condition
cond_cells[[cond]] = simp_allcells_css[,simp_allcells_css@meta.data$Condition==cond]
# define metadata
meta_simp = data.frame(row.names = rownames(cond_cells[[cond]]@meta.data),
"Cell" = rownames(cond_cells[[cond]]@meta.data),
"cell_type" = as.character(cond_cells[[cond]]@meta.data$subpops_simp),
stringsAsFactors = F)
write.table(meta_simp, file = paste0(cpdb_path, "/", cond, "_simp/", cond, "_meta_names.txt"),
sep = "\t", col.names = T, row.names = F, quote = F)
# normalise and save
cond_cells[[cond]] = suppressWarnings(SCTransform(cond_cells[[cond]], do.correct.umi = T,
vars.to.regress=c("unique_name", "nCount_RNA"),
variable.features.rv.th = 1, seed.use = 1,
return.only.var.genes = F, verbose = F,
variable.features.n = NULL))
dat = cbind(rownames(cond_cells[[cond]]@assays$SCT@data),
Matrix::as.matrix(cond_cells[[cond]]@assays$SCT@data))
colnames(dat)[1] = "Gene"
write.table(dat, file = paste0(cpdb_path, "/", cond, "_simp/", cond, "_exp_norm.txt"),
sep = "\t", col.names = T, row.names = F, quote = F)
}Warning: sparse->dense coercion: allocating vector of size 3.3 GiBWarning: sparse->dense coercion: allocating vector of size 3.2 GiBWarning: sparse->dense coercion: allocating vector of size 8.8 GiBWarning: sparse->dense coercion: allocating vector of size 8.7 GiBWarning: sparse->dense coercion: allocating vector of size 7.8 GiBWarning: sparse->dense coercion: allocating vector of size 7.7 GiBfor(n in names(cond_cells)){
comm1 = paste0("cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, " --project-name ", n, " --counts-data gene_name")
comm2 = paste0("cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "/", n, " results/cell_comm/CellPhoneDB_V3/")
comm3 = paste0("cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt results/cell_comm/CellPhoneDB_V3/", n, "/")
comm4 = paste0("cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/", n, "/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/", n, "/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt results/cell_comm/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt")
print(n)
print(comm1)
print(comm2)
print(comm3)
print(comm4)
print(".")
}[1] "healthy"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy/healthy_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy/healthy_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/healthy --project-name healthy --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/healthy/healthy results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy/healthy_meta_names.txt results/cell_comm/CellPhoneDB_V3/healthy/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/healthy/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/healthy/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt results/cell_comm/CellPhoneDB_V3/healthy/healthy_meta_names.txt"
[1] "."
[1] "embolised"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised/embolised_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised/embolised_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/embolised --project-name embolised --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/embolised/embolised results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised/embolised_meta_names.txt results/cell_comm/CellPhoneDB_V3/embolised/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/embolised/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/embolised/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt results/cell_comm/CellPhoneDB_V3/embolised/embolised_meta_names.txt"
[1] "."
[1] "regenerating"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating/regenerating_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating/regenerating_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/regenerating --project-name regenerating --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/regenerating/regenerating results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating/regenerating_meta_names.txt results/cell_comm/CellPhoneDB_V3/regenerating/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/regenerating/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/regenerating/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt results/cell_comm/CellPhoneDB_V3/regenerating/regenerating_meta_names.txt"
[1] "."Process results
Load results
for(n in names(cond_cells)){
comm1 = paste0("cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "_simp --project-name ", n, "_simp --counts-data gene_name")
comm2 = paste0("cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "_simp/", n, "_simp results/cell_comm/CellPhoneDB_V3/")
comm3 = paste0("cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt results/cell_comm/CellPhoneDB_V3/", n, "_simp/")
comm4 = paste0("cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/", n, "_simp/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/", n, "_simp/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt")
print(n)
print(comm1)
print(comm2)
print(comm3)
print(comm4)
print(".")
}[1] "healthy"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy_simp/healthy_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy_simp/healthy_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/healthy_simp --project-name healthy_simp --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/healthy_simp/healthy_simp results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy_simp/healthy_meta_names.txt results/cell_comm/CellPhoneDB_V3/healthy_simp/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/healthy_simp/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/healthy_simp/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/healthy_simp/healthy_meta_names.txt"
[1] "."
[1] "embolised"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised_simp/embolised_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised_simp/embolised_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/embolised_simp --project-name embolised_simp --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/embolised_simp/embolised_simp results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised_simp/embolised_meta_names.txt results/cell_comm/CellPhoneDB_V3/embolised_simp/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/embolised_simp/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/embolised_simp/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/embolised_simp/embolised_meta_names.txt"
[1] "."
[1] "regenerating"
[1] "cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating_simp/regenerating_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating_simp/regenerating_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/regenerating_simp --project-name regenerating_simp --counts-data gene_name"
[1] "cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/regenerating_simp/regenerating_simp results/cell_comm/CellPhoneDB_V3/"
[1] "cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating_simp/regenerating_meta_names.txt results/cell_comm/CellPhoneDB_V3/regenerating_simp/"
[1] "cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/regenerating_simp/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/regenerating_simp/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/regenerating_simp/regenerating_meta_names.txt"
[1] "."Reformat significant means matrix
cpdb_path = "results/cell_comm/CellPhoneDB_V3"
sig_means_names_l = list()
dec_l = list()
net_names_l = list()
meta_l = list()
conds = unique(allcells_css@meta.data$Condition)
for(n in c(conds, paste0(conds, "_simp"))){
ns = strsplit(n, "_")[[1]][1]
sig_means_names_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/significant_means.txt"),
header = T, sep = "\t", stringsAsFactors = F)
dec_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/deconvoluted.txt"),
header = T, sep = "\t")
colnames(dec_l[[n]]) = gsub(".", " ", colnames(dec_l[[n]]), fixed = T)
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="gd T cells"] = "gd-T cells"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Macrophages HES4 "] = "Macrophages (HES4+)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells"] = "CD8 ab-T cells"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 1"] = "CD8 ab-T cells 1"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 2"] = "CD8 ab-T cells 2"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 3"] = "CD8 ab-T cells 3"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Naive CD4 T cells"] = "Naive CD4+ T cells"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="ab T cells stress "] = "ab-T cells (stress)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes secretory "] = "Monocytes (secretory)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes TREM2 CD9 "] = "Monocytes (TREM2+ CD9+)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes IGSF21 GPR34 "] = "Monocytes (IGSF21+ GPR34+)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC stress "] = "LSEC (stress)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC remodelling "] = "LSEC (remodelling)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC interferon "] = "LSEC (interferon)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC high MT 2 "] = "LSEC (high MT 2)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC high MT 1 "] = "LSEC (high MT 1)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC high MT "] = "LSEC (high MT)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC fenestr "] = "LSEC (fenestr.)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Kupffer cells SUCNR1 "] = "Kupffer cells (SUCNR1+)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="IgG Plasma cells"] = "IgG+ Plasma cells"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="IgA Plasma cells"] = "IgA+ Plasma cells"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells stress "] = "CD8 ab-T cells (stress)"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="EC non LSEC"] = "EC non-LSEC"
colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Dividing T NK cells"] = "Dividing T/NK cells"
net_names_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/count_net.txt"),
header = T, sep = "\t", stringsAsFactors = F)
meta_l[[n]] = if(!grepl("simp", n)){
read.table(paste0(cpdb_path, "/", n, "/", n, "_meta_names.txt"),header = T,sep = "\t")
} else{
read.table(paste0(cpdb_path, "/", n, "/", ns, "_meta_names.txt"),header = T,sep = "\t")
}
}
saveRDS(dec_l, file = "results/cell_comm/v3/deconvoluted_list.RDS")
saveRDS(net_names_l, file = "results/cell_comm/v3/count_net_list.RDS")Plot interaction counts
reform_list = list()
for(n in names(meta_l)){
simp_reform = reshape2::melt(sig_means_names_l[[n]][,c(1:4,7:9,13:ncol(sig_means_names_l[[n]]))])
simp_reform = simp_reform[complete.cases(simp_reform),]
lr1 = c()
lr2 = c()
for(i in 1:nrow(simp_reform)){
if(grepl("complex", simp_reform$partner_a[i])){
lr1 = c(lr1, substr(simp_reform$partner_a[i], 9,100))
} else{
lr1 = c(lr1, strsplit(simp_reform$interacting_pair[i], "_")[[1]][1])
}
if(grepl("complex", simp_reform$partner_b[i])){
lr2 = c(lr2, substr(simp_reform$partner_b[i], 9,100))
} else{
spltlen = length(strsplit(simp_reform$interacting_pair[i], "_")[[1]])
lr2 = c(lr2, strsplit(simp_reform$interacting_pair[i], "_")[[1]][spltlen])
}
}
simp_reform$lr1 = lr1
simp_reform$lr2 = lr2
simp_reform$ct1 = NA
simp_reform$ct2 = NA
donest = rep(NA, length(simp_reform$ct1))
doneen = rep(NA, length(simp_reform$ct2))
for(ct in sort(unique(meta_l[[n]]$cell_type), decreasing = T)){
ct_mod = gsub("-", " ", ct, fixed = T)
ct_mod = gsub("+", " ", ct_mod, fixed = T)
ct_mod = gsub("/", " ", ct_mod, fixed = T)
ct_mod = gsub("(", " ", ct_mod, fixed = T)
ct_mod = gsub(")", " ", ct_mod, fixed = T)
ct_mod = gsub(".", " ", ct_mod, fixed = T)
st = grepl(paste0("^",ct_mod, " "), gsub(".", " ", simp_reform$variable, fixed = T), fixed = F)
en = grepl(paste0(" ",ct_mod,"$"), gsub(".", " ", simp_reform$variable, fixed = T), fixed = F)
simp_reform$ct1[st & is.na(donest)] = ct
simp_reform$ct2[en & is.na(doneen)] = ct
donest[st] = T
doneen[en] = T
}
#colnames(dec_l[[n]])[!(colnames(dec_l[[n]]) %in% unique(simp_reform$ct2))]
# direction: receptors activate internal signal - that is the direction of the signalling
## if both are true, the "net signal" is 0
simp_reform$dir = ifelse(simp_reform$receptor_a==simp_reform$receptor_b, 0,
ifelse(simp_reform$receptor_a=="True" &
simp_reform$receptor_b=="False", -1,
ifelse(simp_reform$receptor_a=="False" &
simp_reform$receptor_b=="True", 1, NA)))
simp_reform = simp_reform[,c("id_cp_interaction", "ct1", "ct2", "lr1", "lr2",
"value", "dir")]
for(i in 1:nrow(simp_reform)){
if(simp_reform[i,"dir"]==-1){
tmp = simp_reform[i,"ct2"]
simp_reform[i,"ct2"] = simp_reform[i,"ct1"]
simp_reform[i,"ct1"] = tmp
tmp = simp_reform[i,"lr2"]
simp_reform[i,"lr2"] = simp_reform[i,"lr1"]
simp_reform[i,"lr1"] = tmp
simp_reform[i,"dir"]=1
}
}
reform_list[[n]] = simp_reform
}Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variables
Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variables
Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variables
Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variables
Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variables
Using id_cp_interaction, interacting_pair, partner_a, partner_b, secreted, receptor_a, receptor_b as id variablessaveRDS(reform_list, file = "results/cell_comm/v3/reform_list.RDS")Making two cell type by interaction matrices: one has the ligand mean, another the receptor mean
inter_counts_l = list()
for(n in names(net_names_l)){
inter_counts = reshape2::dcast(data = net_names_l[[n]],
formula = SOURCE~TARGET, value.var = "count")
rownames(inter_counts) = inter_counts[,1]
inter_counts = inter_counts[,-1]
pdf(paste0("results/cell_comm/v3/", n, "_number_of_interactions.pdf"), useDingbats = F,
height = 10, width = 10)
pheatmap::pheatmap(inter_counts, clustering_method = "ward.D2", main = "All clusters")
dev.off()
inter_counts_l[[n]] = inter_counts
}Making two cell type by interaction matrices: one has the ligand mean, another the receptor mean (here only directional)
scoring_mats = list()
for(n in names(reform_list)){
cl_reform = reform_list[[n]]
# add reversed non-directed interactions, to consider them in both directions
cl_reform0 = cl_reform[cl_reform$dir==0,]
cl_reform0 = cl_reform0[,c(1,3,2,5,4,6,7)]
colnames(cl_reform0) = colnames(cl_reform)
cl_reform = rbind(cl_reform, cl_reform0)
dec_cl = dec_l[[n]]
mat_lig_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)),
ncol = length(unique(cl_reform$ct1))))
mat_rec_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)),
ncol = length(unique(cl_reform$ct1))))
colnames(mat_lig_cl) = colnames(mat_rec_cl) = unique(cl_reform$ct1)
rownames(mat_lig_cl) = rownames(mat_rec_cl) = unique(cl_reform$id_cp_interaction)
for(i in 1:nrow(cl_reform)){
g1 = cl_reform[i,"lr1"]
g2 = cl_reform[i,"lr2"]
c1 = cl_reform[i,"ct1"]
c2 = cl_reform[i,"ct2"]
int = as.character(cl_reform[i,1])
m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g1,c1], na.rm = T)
if(is.na(m1)) m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g1,c1],
na.rm = T)
mat_lig_cl[int, c1] = m1
m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g2,c2], na.rm = T)
if(is.na(m2)) m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g2,c2],
na.rm = T)
if(is.na(m1) | is.na(m2)) print(int)
mat_rec_cl[int, c2] = m2
}
# ligand vs receptor correlation
cor_mat_each_cl = cor(mat_lig_cl, mat_rec_cl, use="pairwise.complete.obs", method = "sp")
scoring_mats[[n]] = list("mat_lig_ct" = mat_lig_cl, "mat_rec_ct" = mat_rec_cl,
"cor_mat_each" = cor_mat_each_cl)
# sum(lig, reg) correlation
mat_lig_cl[is.na(mat_lig_cl)] = 0
mat_rec_cl[is.na(mat_rec_cl)] = 0
mat_both_cl = mat_lig_cl+mat_rec_cl
mat_both_cl[mat_both_cl==0] = NA
cor_mat_both_cl = cor(mat_both_cl, use="pairwise.complete.obs", method = "sp")
scoring_mats[[n]]$cor_mat_both = cor_mat_both_cl
}
saveRDS(scoring_mats, file = "results/cell_comm/v3/scoring_mats.RDS")Count interactions of each type
scoring_mats_dir = list()
for(n in names(reform_list)){
cl_reform = reform_list[[n]]
# add reversed non-directed interactions, to consider them in both directions
cl_reform0 = cl_reform[cl_reform$dir==0,]
cl_reform0 = cl_reform0[,c(1,3,2,5,4,6,7)]
colnames(cl_reform0) = colnames(cl_reform)
cl_reform = rbind(cl_reform, cl_reform0)
cl_reform = cl_reform[cl_reform$dir==1,]
dec_cl = dec_l[[n]]
mat_lig_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)),
ncol = length(unique(cl_reform$ct1))))
mat_rec_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)),
ncol = length(unique(cl_reform$ct1))))
colnames(mat_lig_cl) = colnames(mat_rec_cl) = unique(cl_reform$ct1)
rownames(mat_lig_cl) = rownames(mat_rec_cl) = unique(cl_reform$id_cp_interaction)
for(i in 1:nrow(cl_reform)){
g1 = cl_reform[i,"lr1"]
g2 = cl_reform[i,"lr2"]
c1 = cl_reform[i,"ct1"]
c2 = cl_reform[i,"ct2"]
int = as.character(cl_reform[i,1])
m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g1,c1], na.rm = T)
if(is.na(m1)) m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g1,c1],
na.rm = T)
mat_lig_cl[int, c1] = m1
m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g2,c2], na.rm = T)
if(is.na(m2)) m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g2,c2],
na.rm = T)
if(is.na(m1) | is.na(m2)) print(int)
mat_rec_cl[int, c2] = m2
}
# ligand vs receptor correlation
cor_mat_each_cl = cor(mat_lig_cl, mat_rec_cl, use="pairwise.complete.obs", method = "sp")
scoring_mats_dir[[n]] = list("mat_lig_ct" = mat_lig_cl, "mat_rec_ct" = mat_rec_cl,
"cor_mat_each" = cor_mat_each_cl)
# sum(lig, reg) correlation
mat_lig_cl[is.na(mat_lig_cl)] = 0
mat_rec_cl[is.na(mat_rec_cl)] = 0
mat_both_cl = mat_lig_cl+mat_rec_cl
mat_both_cl[mat_both_cl==0] = NA
cor_mat_both_cl = cor(mat_both_cl, use="pairwise.complete.obs", method = "sp")
scoring_mats_dir[[n]]$cor_mat_both = cor_mat_both_cl
}Warning: the standard deviation is zerosaveRDS(scoring_mats_dir, file = "results/cell_comm/v3/scoring_mats_dir.RDS")Interaction analysis in conditions
Compare interactions with DE genes
inter_counts_dir = list()
for(n in names(reform_list)){
inter_counts_dir[[n]] = list()
for(i in c(0,1)){
cl_reform = reform_list[[n]]
cl_reform = cl_reform[cl_reform$dir==i,]
n_dir_int = matrix(0, nrow = length(unique(cl_reform$ct1)),
ncol = length(unique(cl_reform$ct1)))
rownames(n_dir_int) = colnames(n_dir_int) = unique(cl_reform$ct1)
for(c1 in unique(cl_reform$ct1)){
for(c2 in unique(cl_reform$ct1)){
n_dir_int[c1,c2] = nrow(cl_reform[cl_reform$ct1==c1 & cl_reform$ct2==c2,])
if(i==0){
n_dir_int[c1,c2] = n_dir_int[c1,c2]+nrow(cl_reform[cl_reform$ct1==c2 &
cl_reform$ct2==c1,])
}
}
}
inter_counts_dir[[n]][[as.character(i)]] = n_dir_int
}
}
saveRDS(inter_counts_dir, file = "results/cell_comm/v3/inter_counts_dir.RDS")Plot interaction heatmap - total and filtered
# add genes from complexes
complex_ref = unique(rbind(dec_l$healthy[dec_l$healthy$is_complex=="True",c(1,5)],
dec_l$embolised[dec_l$embolised$is_complex=="True",c(1,5)],
dec_l$regenerating[dec_l$regenerating$is_complex=="True",c(1,5)],
dec_l$healthy_simp[dec_l$healthy_simp$is_complex=="True",c(1,5)],
dec_l$embolised_simp[dec_l$embolised_simp$is_complex=="True",c(1,5)],
dec_l$regenerating_simp[dec_l$regenerating_simp$is_complex=="True",c(1,5)]))
inter_df = list()
for(n in names(reform_list)){
tmp = merge(reform_list[[n]], complex_ref, by.x = 4, by.y = 2, all.x = T)[,c(2,3,4,1,5,8,6,7)]
inter_df[[n]] = merge(tmp, complex_ref, by.x = 5, by.y = 2, all.x = T)[,c(2,3,4,5,6,1,9,7,8)]
inter_df[[n]]$gene_name.x[is.na(inter_df[[n]]$gene_name.x)] = inter_df[[n]]$lr1[is.na(inter_df[[n]]$gene_name.x)]
inter_df[[n]]$gene_name.y[is.na(inter_df[[n]]$gene_name.y)] = inter_df[[n]]$lr2[is.na(inter_df[[n]]$gene_name.y)]
colnames(inter_df[[n]])[c(5,7)] = c("gn1", "gn2")
}
for(cc in names(inter_df)){
# interaction unique in condition
unique_inter = setdiff(unique(inter_df[[cc]]$id_cp_interaction),
unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$id_cp_interaction,
inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$id_cp_interaction)))
inter_df[[cc]]$cpdb_unique = inter_df[[cc]]$id_cp_interaction %in% unique_inter
# lr1 unique in condition
unique_lr1 = setdiff(unique(inter_df[[cc]]$lr1),
unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$lr1,
inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$lr1)))
inter_df[[cc]]$cpdb_unique_lr1 = inter_df[[cc]]$lr1 %in% unique_lr1
# lr2 unique in condition
unique_lr2 = setdiff(unique(inter_df[[cc]]$lr2),
unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$lr2,
inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$lr2)))
inter_df[[cc]]$cpdb_unique_lr2 = inter_df[[cc]]$lr2 %in% unique_lr2
}
for(cc in names(inter_df)){
oc1 = names(inter_df)[names(inter_df)!=cc][1]
oc2 = names(inter_df)[names(inter_df)!=cc][2]
int1 = paste0(inter_df[[cc]]$id_cp_interaction,inter_df[[cc]]$ct1,inter_df[[cc]]$ct2)
int2 = unique(paste0(inter_df[[oc1]]$id_cp_interaction,inter_df[[oc1]]$ct1,inter_df[[oc1]]$ct2))
int3 = unique(paste0(inter_df[[oc2]]$id_cp_interaction,inter_df[[oc2]]$ct1,inter_df[[oc2]]$ct2))
unique_inter = setdiff(unique(int1), unique(c(int2, int3)))
inter_df[[cc]]$cpdb_unique_ct = int1 %in% unique_inter
}
for(n in names(inter_df)){
df = inter_df[[n]]
df = unique(df[,c(1:3)])
ints_un_ct = rowSums(table(df$id_cp_interaction, df$ct1)>0)<4 |
rowSums(table(df$id_cp_interaction, df$ct2)>0)<4
inter_df[[n]]$inter_ct_spec = inter_df[[n]]$id_cp_interaction %in% names(ints_un_ct)[ints_un_ct]
}
for(n in names(inter_df)){
xxx = data.frame(ct = c(inter_df[[n]][,2], inter_df[[n]][,3]),
lr = c(inter_df[[n]][,4], inter_df[[n]][,6]))
xxx = unique(xxx)
tab_lr = (table(xxx$lr, xxx$ct)>0)*1
tab_lr = rowSums(tab_lr)
inter_df[[n]]$lr1_nct = tab_lr[inter_df[[n]]$lr1]
inter_df[[n]]$lr2_nct = tab_lr[inter_df[[n]]$lr2]
}
saveRDS(inter_df, file = "results/cell_comm/v3/cond_diff_interact_DE.RDS")Get non-ubiquitous interactions
int_counts_total = list()
int_counts_filt = list()
for(n in names(inter_df)){
subdfct = unique(inter_df[[n]][,1:3])
subdfct = unique(subdfct)
dfct = data.frame("ct1" = c(subdfct$ct1, subdfct$ct2),
"ct2" = c(subdfct$ct2, subdfct$ct1))
int_counts_total[[n]] = dfct
pheatmap::pheatmap(table(dfct$ct1, dfct$ct2), main = n)
keep = inter_df[[n]]$lr1_nct<=1 | inter_df[[n]]$lr2_nct<=1
subdfct = unique(inter_df[[n]][keep,c(1:3, 15,16)])
swp = subdfct[,5]==1
tmp = subdfct$ct2[swp]
subdfct$ct2[swp] = subdfct$ct1[swp]
subdfct$ct1[swp] = tmp
tmp = subdfct$lr2_nct[swp]
subdfct$lr2_nct[swp] = subdfct$lr1_nct[swp]
subdfct$lr1_nct[swp] = tmp
dup = subdfct[subdfct$lr1_nct==1 & subdfct$lr2_nct==1,]
tmp = dup$ct2
dup$ct2 = dup$ct1
dup$ct1 = tmp
subdfct = rbind(subdfct, dup)
subdfct = unique(subdfct[,1:3])
int_counts_filt[[n]] = dfct
pheatmap::pheatmap(table(subdfct$ct1, subdfct$ct2), main = n)
}
saveRDS(int_counts_total, file = "results/cell_comm/v3/int_counts_total.RDS")
saveRDS(int_counts_filt, file = "results/cell_comm/v3/int_counts_filt.RDS")Annotate interactions
inter_nonss = setdiff(unique(c(inter_df$embolised$id_cp_interaction,
inter_df$regenerating$id_cp_interaction)),
unique(inter_df$healthy$id_cp_interaction))
inter_nonemb = setdiff(unique(c(inter_df$healthy$id_cp_interaction,
inter_df$regenerating$id_cp_interaction)),
unique(inter_df$embolised$id_cp_interaction))
inter_nonreg = setdiff(unique(c(inter_df$embolised$id_cp_interaction,
inter_df$healthy$id_cp_interaction)),
unique(inter_df$regenerating$id_cp_interaction))
ct_g_cond = list()
for(n in names(inter_df)[1:3]){
df = inter_df[[n]][inter_df[[n]]$cpdb_unique |
inter_df[[n]]$id_cp_interaction %in% c(inter_nonss, inter_nonemb, inter_nonreg),]
#df = df[df$lr1_nct<=1 | df$lr2_nct<=1,]
cts = unique(c(df$ct1, df$ct2))
ct_g_list = list()
for(ct in cts){
ct_g_list[[ct]] = data.frame("interact" = c(as.character(df$id_cp_interaction[df$ct1==ct]),
as.character(df$id_cp_interaction[df$ct2==ct])),
"gene" = c(as.character(df$gn1[df$ct1==ct]),
as.character(df$gn2[df$ct2==ct])),
"gene_target" = c(as.character(df$gn2[df$ct1==ct]),
as.character(df$gn1[df$ct2==ct])),
"ct" = ct,
"ct_target" = c(as.character(df$ct2[df$ct1==ct]),
as.character(df$ct1[df$ct2==ct])),
"cond" = n, stringsAsFactors = F)
}
ct_g_cond[[n]] = unique(Reduce(rbind, ct_g_list))
}
ct_g_cond = Reduce(rbind, ct_g_cond)
dim(ct_g_cond)[1] 23151 6length(unique(ct_g_cond$interact))[1] 249inter_nonss = setdiff(unique(c(inter_df$embolised_simp$id_cp_interaction,
inter_df$regenerating_simp$id_cp_interaction)),
unique(inter_df$healthy_simp$id_cp_interaction))
inter_nonemb = setdiff(unique(c(inter_df$healthy_simp$id_cp_interaction,
inter_df$regenerating_simp$id_cp_interaction)),
unique(inter_df$embolised_simp$id_cp_interaction))
inter_nonreg = setdiff(unique(c(inter_df$embolised_simp$id_cp_interaction,
inter_df$healthy_simp$id_cp_interaction)),
unique(inter_df$regenerating_simp$id_cp_interaction))
cts_g_cond = list()
for(n in names(inter_df)[4:6]){
df = inter_df[[n]][inter_df[[n]]$cpdb_unique |
inter_df[[n]]$id_cp_interaction %in% c(inter_nonss, inter_nonemb, inter_nonreg),]
#df = df[df$lr1_nct<=1 | df$lr2_nct<=1,]
cts = unique(c(df$ct1, df$ct2))
ct_g_list = list()
for(ct in cts){
ct_g_list[[ct]] = data.frame("interact" = c(as.character(df$id_cp_interaction[df$ct1==ct]),
as.character(df$id_cp_interaction[df$ct2==ct])),
"gene" = c(as.character(df$gn1[df$ct1==ct]),
as.character(df$gn2[df$ct2==ct])),
"gene_target" = c(as.character(df$gn2[df$ct1==ct]),
as.character(df$gn1[df$ct2==ct])),
"ct" = ct,
"ct_target" = c(as.character(df$ct2[df$ct1==ct]),
as.character(df$ct1[df$ct2==ct])),
"cond" = n, stringsAsFactors = F)
}
cts_g_cond[[n]] = unique(Reduce(rbind, ct_g_list))
}
cts_g_cond = Reduce(rbind, cts_g_cond)
dim(cts_g_cond)[1] 19383 6length(unique(cts_g_cond$interact))[1] 253ct_g_l = list("cts_g_cond" = cts_g_cond,
"ct_g_cond" = ct_g_cond)Get expression for each interaction in each condition
#xxx = merge(unique(rbind(ct_g_cond[,1:3], cts_g_cond[,1:3])),
# unique(inter_annot[,c(1,4)]), by = 1, all.x = T)
#write.csv(xxx, file = "results/cell_comm/v3/inter_unique5.csv", row.names = F, col.names = T, quote = F)
inter_annot = read.csv("results/cell_comm/updt/inter_unique5.csv", header = T, stringsAsFactors = F)
inter_annot = unique(inter_annot[,c(1,4)])
description = strsplit(inter_annot$description, ";")
inter_des = lapply(1:length(description),
function(x) rep(inter_annot$interact[x], length(description[[x]])))
inter_annot = data.frame("inter" = unlist(inter_des),
"funct" = unlist(description))
inter_annot$funct[inter_annot$funct=="intercellular adhesion"] = "adhesion"
inter_annot$funct[inter_annot$funct=="antibody regulation"] = "immune regulation"
inter_annot$funct[inter_annot$funct=="antigen presentation"] = "immune activity"
write.csv(inter_annot, file = "data/interaction_annotation2.csv",
row.names = F, col.names = T, quote = F)Warning: attempt to set 'col.names' ignoredcolnames(inter_annot) = c("interact", "description")
ct_g_cond_ann_l = list()
for(n in names(ct_g_l)){
ct_g_cond_ann = merge(ct_g_l[[n]], inter_annot, by = 1, all.x = T)
ct_g_cond_ann = unique(ct_g_cond_ann[,c(1,4,5,6,7)])
ct_g_cond_ann = data.frame("interactions" = rep(as.character(ct_g_cond_ann$interact),2),
"celltypes" = c(as.character(ct_g_cond_ann$ct),
as.character(ct_g_cond_ann$ct_target)),
"condition" = rep(as.character(ct_g_cond_ann$cond),2),
"description" = rep(as.character(ct_g_cond_ann$description),2))
ct_g_cond_ann$condition = factor(unlist(lapply(strsplit(ct_g_cond_ann$condition, "_"),
function(x) x[1])),
levels = c("healthy", "embolised", "regenerating"))
plt_bar_func = ggplot(ct_g_cond_ann, aes(x = condition, fill = condition))+
facet_grid(description~celltypes, scales = "free_y")+
geom_bar()+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
legend.position = "none")
pdf(paste0("results/cell_comm/v3/", n, "_plt_bar_func.pdf"), height = 20, width = 50)
print(plt_bar_func)
dev.off()
ct_g_cond_ann_l[[n]] = ct_g_cond_ann
saveRDS(ct_g_cond_ann, file = paste0("results/cell_comm/v3/", n, "_ann.RDS"))
}Variability of interactions
exp_list = list()
int_groups = list("cts_g_cond" = unique(c(sig_means_names_l$healthy_simp$id_cp_interaction,
sig_means_names_l$embolised_simp$id_cp_interaction,
sig_means_names_l$regenerating_simp$id_cp_interaction)),
"ct_g_cond" = unique(c(sig_means_names_l$healthy$id_cp_interaction,
sig_means_names_l$embolised$id_cp_interaction,
sig_means_names_l$regenerating$id_cp_interaction)))
for(n in names(sig_means_names_l)){
df = sig_means_names_l[[n]][,c(1,5:6,13:ncol(sig_means_names_l[[n]]))]
#colnames(df) = gsub(".", " ", colnames(df), fixed = T)
colnames(df) = gsub("gd.T.cells", "gd-T cells", colnames(df), fixed = T)
colnames(df) = gsub("B.cells", "B cells", colnames(df), fixed = T)
colnames(df) = gsub("Dividing.endothelial.cells", "Dividing endothelial cells",
colnames(df), fixed = T)
colnames(df) = gsub("Dividing.cDCs", "Dividing cDCs", colnames(df), fixed = T)
colnames(df) = gsub("Infiltrating.NK.cells", "Infiltrating NK cells", colnames(df), fixed = T)
colnames(df) = gsub("Macrophages..HES4..", "Macrophages (HES4+)", colnames(df), fixed = T)
colnames(df) = gsub("activated.DCs", "activated DCs", colnames(df), fixed = T)
colnames(df) = gsub("Pericentral.LSEC", "Pericentral LSEC", colnames(df), fixed = T)
colnames(df) = gsub("Midzonal.LSEC", "Midzonal LSEC", colnames(df), fixed = T)
colnames(df) = gsub("Periportal.LSEC", "Periportal LSEC", colnames(df), fixed = T)
colnames(df) = gsub("Periportal.LSEC", "Periportal LSEC", colnames(df), fixed = T)
colnames(df) = gsub("NK.cells.1", "NK cells 1", colnames(df), fixed = T)
colnames(df) = gsub("NK.cells.2", "NK cells 2", colnames(df), fixed = T)
colnames(df) = gsub("NK.cells.3", "NK cells 3", colnames(df), fixed = T)
colnames(df) = gsub("NK.cells", "NK cells", colnames(df), fixed = T)
colnames(df) = gsub("CD8.ab.T.cells.1", "CD8 ab-T cells 1", colnames(df), fixed = T)
colnames(df) = gsub("CD8.ab.T.cells.2", "CD8 ab-T cells 2", colnames(df), fixed = T)
colnames(df) = gsub("CD8.ab.T.cells.3", "CD8 ab-T cells 3", colnames(df), fixed = T)
colnames(df) = gsub("CD8.ab.T.cells", "CD8 ab-T cells", colnames(df), fixed = T)
colnames(df) = gsub("Naive.CD4..T.cells", "Naive CD4+ T cells", colnames(df), fixed = T)
colnames(df) = gsub("ab.T.cells..stress.", "ab-T cells (stress)", colnames(df), fixed = T)
colnames(df) = gsub("ab.T.cells..stress.", "ab-T cells (stress)", colnames(df), fixed = T)
colnames(df) = gsub("Fibroblasts.1", "Fibroblasts 1", colnames(df), fixed = T)
colnames(df) = gsub("Fibroblasts.2", "Fibroblasts 2", colnames(df), fixed = T)
colnames(df) = gsub("Fibroblasts.3", "Fibroblasts 3", colnames(df), fixed = T)
colnames(df) = gsub("Monocytes..secretory.", "Monocytes (secretory)", colnames(df), fixed = T)
colnames(df) = gsub("Monocytes..TREM2..CD9..", "Monocytes (TREM2+ CD9+)", colnames(df), fixed = T)
colnames(df) = gsub("Monocytes..IGSF21..GPR34..", "Monocytes (IGSF21+ GPR34+)",
colnames(df), fixed = T)
colnames(df) = gsub("LSEC..stress.", "LSEC (stress)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..remodelling.", "LSEC (remodelling)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..interferon.", "LSEC (interferon)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..high.MT.2.", "LSEC (high MT 2)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..high.MT.1.", "LSEC (high MT 1)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..high.MT.", "LSEC (high MT)", colnames(df), fixed = T)
colnames(df) = gsub("LSEC..fenestr..", "LSEC (fenestr.)", colnames(df), fixed = T)
colnames(df) = gsub("Kupffer.cells..SUCNR1..", "Kupffer cells (SUCNR1+)", colnames(df), fixed = T)
colnames(df) = gsub("IgG..Plasma.cells", "IgG+ Plasma cells", colnames(df), fixed = T)
colnames(df) = gsub("IgA..Plasma.cells", "IgA+ Plasma cells", colnames(df), fixed = T)
colnames(df) = gsub("EC.non.LSEC", "EC non-LSEC", colnames(df), fixed = T)
colnames(df) = gsub("Dividing.T.NK.cells", "Dividing T/NK cells", colnames(df), fixed = T)
colnames(df) = gsub("Kupffer.cells", "Kupffer cells", colnames(df), fixed = T)
colnames(df) = gsub("TRM.cells", "TRM cells", colnames(df), fixed = T)
colnames(df) = gsub("MAIT.cells.1", "MAIT cells 1", colnames(df), fixed = T)
colnames(df) = gsub("MAIT.cells.2", "MAIT cells 2", colnames(df), fixed = T)
colnames(df) = gsub("MAIT.cells", "MAIT cells", colnames(df), fixed = T)
colnames(df) = gsub("Lymphatic.EC", "Lymphatic EC", colnames(df), fixed = T)
colnames(df) = gsub("Stellate.cells", "Stellate cells", colnames(df), fixed = T)
# head(colnames(df), 60)
df = reshape2::melt(df, id.vars = 1:3)
df$value[is.na(df$value)] = 0
df = df %>%
group_by(id_cp_interaction, gene_a, gene_b, variable) %>%
summarise(value=(mean(value)), .groups = "keep")
df = as.data.frame(df)
ns = if(grepl("simp", n)) "cts_g_cond" else "ct_g_cond"
ct_g_cond_ann = ct_g_cond_ann_l[[ns]]
sub_df = df[df$id_cp_interaction %in% ct_g_cond_ann$interactions,]
sub_df = merge(ct_g_l[[ns]], sub_df, all = T, by = 1)
sub_df = sub_df[sub_df$interact %in% int_groups[[ns]],]
sub_df$variable = as.character(sub_df$variable)
sub_df$value[is.na(sub_df$value)] = 0
ct1 = unlist(lapply(strsplit(sub_df$variable, ".", fixed = T), function(x) x[1]))
ct2 = unlist(lapply(strsplit(sub_df$variable, ".", fixed = T), function(x) x[2]))
keep = sapply(1:nrow(sub_df), function(x) (sub_df$ct[x]==ct1[x] & sub_df$ct_target[x]==ct2[x]) |
(sub_df$ct[x]==ct2[x] & sub_df$ct_target[x]==ct1[x]))
exp_list[[n]] = sub_df[keep,c(1:6,10)]
}
for(n in names(exp_list)){
exp_list[[n]] = exp_list[[n]][!is.na(exp_list[[n]]$interact),]
}
ct_int_exp_l = list()
for(simp in c(T, F)){
n_use = names(exp_list)[grepl("simp", names(exp_list))==simp]
#ct_int_exp = cbind(exp_list[[n_use[1]]], exp_list[[n_use[3]]]$value, exp_list[[n_use[3]]]$value)
ct_int_exp = merge(exp_list[[n_use[1]]], exp_list[[n_use[2]]], by = 1:6)
ct_int_exp = merge(ct_int_exp, exp_list[[n_use[3]]], by = 1:6)
ct_int_exp$value.x[is.na(ct_int_exp$value.x)] = ct_int_exp$value.y[is.na(ct_int_exp$value.y)] = ct_int_exp$value[is.na(ct_int_exp$value)] = 0
colnames(ct_int_exp)[7:9] = c("healthy_exp", "embolised_exp", "regenerating_exp")
ct_int_exp = ct_int_exp[ct_int_exp$healthy_exp>0 |
ct_int_exp$embolised_exp>0 |
ct_int_exp$regenerating_exp>0,]
tup_list = list()
keep_row = c()
for(i in 1:nrow(ct_int_exp)){
tup1 = paste(c(ct_int_exp$gene[i], ct_int_exp$gene_target[i], ct_int_exp$ct[i],
ct_int_exp$ct_target[i], ct_int_exp$cond[i]), collapse = " ")
tup2 = paste(c(ct_int_exp$gene_target[i], ct_int_exp$gene[i], ct_int_exp$ct_target[i],
ct_int_exp$ct[i], ct_int_exp$cond[i]), collapse = " ")
if(!(tup1 %in% tup_list) & !(tup2 %in% tup_list)){
tup_list = c(tup_list, tup1, tup2)
keep_row = c(keep_row, T)
} else{
keep_row = c(keep_row, F)
}
}
ct_int_exp = ct_int_exp[keep_row,]
ct_int_exp = merge(ct_int_exp, unique(ct_g_cond_ann[,c(1,4)]), by = 1, all = T)
nn = if(simp) "simp" else "all"
ct_int_exp_l[[nn]] = ct_int_exp
ct_int_exp_l[[nn]] = ct_int_exp_l[[nn]][complete.cases(ct_int_exp_l[[nn]]),]
ct_int_exp_l[[nn]]$cond = unlist(lapply(strsplit(ct_int_exp_l[[nn]]$cond, "_"), function(x) x[1]))
}
saveRDS(ct_int_exp_l, file = "results/cell_comm/v3/interact_celltype_exp_group_list.RDS")GSEA of interactions using mutual information
inter_annot = read.csv("data/interaction_annotation2.csv", header = T)
inter_annot$funct[grepl("imm", inter_annot$funct)] = "immune"
inter_annot$funct[grepl("inflam", inter_annot$funct)] = "immune"
gr = inter_annot$funct
names(gr) = inter_annot$inter
gr_list = tapply(inter_annot$inter, inter_annot$funct, function(x) x)
her_allint_l = list()
for(simp in c(T, F)){
n_use = names(reform_list)[grepl("simp", names(reform_list))==simp]
he_allint = merge(reform_list[[n_use[1]]][,1:6], reform_list[[n_use[2]]][,1:6], by = 1:3, all = T)
he_allint$lr1.x[is.na(he_allint$lr1.x)] = he_allint$lr1.y[is.na(he_allint$lr1.x)]
he_allint$lr2.x[is.na(he_allint$lr2.x)] = he_allint$lr2.y[is.na(he_allint$lr2.x)]
he_allint = he_allint[,c(1:6,9)]
her_allint = merge(he_allint, reform_list[[n_use[3]]][,1:6], by = 1:3, all = T)
her_allint$lr1.x[is.na(her_allint$lr1.x)] = her_allint$lr1[is.na(her_allint$lr1.x)]
her_allint$lr2.x[is.na(her_allint$lr2.x)] = her_allint$lr2[is.na(her_allint$lr2.x)]
her_allint = her_allint[,c(1:7,10)]
her_allint[is.na(her_allint)] = 0
colnames(her_allint)[4:8] = c("lr1", "lr2", "healthy_exp", "embolised_exp", "regenerating_exp")
# count occurrences per condition. this works bc we're already working with sig means
her_allint = merge(her_allint, data.frame(table(her_allint$id_cp_interaction[her_allint$healthy_exp>0])),
by = 1, all.x = T)
her_allint = merge(her_allint, data.frame(table(her_allint$id_cp_interaction[her_allint$embolised_exp>0])),
by = 1, all.x = T)
her_allint = merge(her_allint,
data.frame(table(her_allint$id_cp_interaction[her_allint$regenerating_exp>0])),
by = 1, all.x = T)
colnames(her_allint)[9:11] = c("healthy_n", "embolised_n", "regenerating_n")
her_allint[is.na(her_allint)] = 0
her_allint$ct_pair = factor(paste0(her_allint$ct1, "_", her_allint$ct2))
comb_cond = combn(colnames(her_allint)[6:8],2)
colnames(comb_cond) = c("he", "hr", "er")
for(i in colnames(comb_cond)){
plot_df = her_allint[her_allint[,comb_cond[1,i]]>0 | her_allint[,comb_cond[2,i]]>0,1:12]
exp_df1 = reshape2::dcast(plot_df, formula = id_cp_interaction ~ ct_pair,
value.var = comb_cond[1,i], fill = 0)
rownames(exp_df1) = exp_df1[,1]
exp_df1 = exp_df1[,-1]>0
exp_df2 = reshape2::dcast(plot_df, formula = id_cp_interaction ~ ct_pair,
value.var = comb_cond[2,i], fill = 0)
rownames(exp_df2) = exp_df2[,1]
exp_df2 = exp_df2[,-1]>0
plot_df = merge(plot_df, sapply(rownames(exp_df1),
function(x) infotheo::mutinformation(exp_df1[x,], exp_df2[x,])),
by.x = 1, by.y = 0, all.x = T)
plot_df = merge(plot_df, sapply(rownames(exp_df1),
function(x) e1071::hamming.distance(exp_df1[x,], exp_df2[x,])),
by.x = 1, by.y = 0, all.x = T)
plot_df = merge(plot_df, sapply(rownames(exp_df1),
function(x) e1071::hamming.distance(exp_df1[x,],
exp_df2[x,])/sum(exp_df1[x,] |
exp_df2[x,])),
by.x = 1, by.y = 0, all.x = T)
plot_df = merge(plot_df, sapply(rownames(exp_df1),
function(x) sum(exp_df1[x,] | exp_df2[x,])),
by.x = 1, by.y = 0, all.x = T)
colnames(plot_df)[13:16] = paste0(c("mutInfo_", "hamm_", "hammNorm_", "tot_"), i)
her_allint = merge(her_allint, unique(plot_df[,c(1,13:16)]), all.x = T, by = 1)
}
her_allint$mutInfo_er[is.na(her_allint$mutInfo_er)] = 1
her_allint$mutInfo_hr[is.na(her_allint$mutInfo_hr)] = 1
her_allint$mutInfo_he[is.na(her_allint$mutInfo_he)] = 1
her_allint$tot_he[is.na(her_allint$tot_he)] = 0
her_allint$tot_hr[is.na(her_allint$tot_hr)] = 0
her_allint$tot_er[is.na(her_allint$tot_er)] = 0
her_allint$hamm_er[is.na(her_allint$hamm_er)] = 0
her_allint$hamm_hr[is.na(her_allint$hamm_hr)] = 0
her_allint$hamm_he[is.na(her_allint$hamm_he)] = 0
her_allint$hammNorm_er[is.na(her_allint$hammNorm_er)] = 0
her_allint$hammNorm_he[is.na(her_allint$hammNorm_he)] = 0
her_allint$hammNorm_hr[is.na(her_allint$hammNorm_hr)] = 0
her_allint$diff_n_he = her_allint$embolised_n-her_allint$healthy_n
her_allint$diff_n_hr = her_allint$regenerating_n-her_allint$healthy_n
her_allint$diff_n_er = her_allint$regenerating_n-her_allint$embolised_n
# plot tot vs mutual
plot_df = unique(her_allint[,c("id_cp_interaction", paste0(c("mutInfo_", "tot_", "diff_n_"), i))])
plt = ggplot(plot_df, aes(x = tot_er, y = mutInfo_er*(diff_n_er/abs(diff_n_er))))+
geom_bin2d()+
scale_x_log10()+
theme_bw()+
theme(aspect.ratio = 1)
print(plt)
nn = if(simp) "simp" else "all"
her_allint_l[[nn]] = her_allint
}Aggregation function missing: defaulting to length
Aggregation function missing: defaulting to length
Warning: column names ‘y.x’, ‘y.y’ are duplicated in the resultAggregation function missing: defaulting to length
Aggregation function missing: defaulting to length
Warning: column names ‘y.x’, ‘y.y’ are duplicated in the resultAggregation function missing: defaulting to length
Aggregation function missing: defaulting to length
Warning: column names ‘y.x’, ‘y.y’ are duplicated in the result
saveRDS(her_allint_l, file = "./results/cell_comm/v3/interactions_mutInfo_condComp.RDS")Save mutual information for LR and cell types
for(n in names(her_allint_l)){
her_allint = her_allint_l[[n]]
comb_cond = combn(colnames(her_allint)[6:8],2)
colnames(comb_cond) = c("he", "hr", "er")
gsea_list = list()
for(i in colnames(comb_cond)){
vals = unique(her_allint[,c("id_cp_interaction", paste0("mutInfo_", i))])
vals2 = vals[,paste0("mutInfo_", i)]
names(vals2) = vals$id_cp_interaction
set.seed(1)
gsea_list[[i]] = liger::bulk.gsea(1-vals2, set.list = gr_list, n.rand = 1000000)
gsea_list[[i]]$group = rownames(gsea_list[[i]])
gsea_list[[i]]$comp = i
}
liger::gsea(1-vals2, geneset = gr_list$immune, plot = T)
# plot GSEA enrichment
plot_df = Reduce(rbind, gsea_list)
plot_df$q.val = -log10(plot_df$q.val+0.0000005)*(plot_df$sscore/abs(plot_df$sscore))
plot_df$comp = factor(plot_df$comp, levels = rev(c("he","hr","er")))
m = tapply(plot_df$q.val, plot_df$group,mean)
plot_df$group = factor(plot_df$group, levels = names(m)[order(m, decreasing = F)])
plt = ggplot(plot_df, aes(x = q.val, y = group, fill = comp))+
geom_col(position = "dodge")+
geom_vline(xintercept = c(log10(0.05), -log10(0.05)), linetype = "dashed")+
geom_vline(xintercept = c(0))+
labs(x = "q-value x score signal", y = "interaction type", fill = "comparison")+
theme_bw()+
theme(axis.text = element_text(colour = "black", size = 7),
axis.title = element_text(colour = "black", size = 7.5),
legend.position = c(0,1),
legend.justification = c(-0.05,1.05),
legend.title = element_text(size = 7.5),
legend.text = element_text(size = 7),
legend.margin = margin(0,0,0,0),
legend.key.size = unit(0.35, "cm"))
print(plt)
saveRDS(gsea_list, file = paste0("./results/cell_comm/v3/", n,
"gsea_interactions_mutInfo_condComp.RDS"))
}
Plot interactions
for(n in names(her_allint_l)){
her_allint = her_allint_l[[n]]
# select genes from lowest mutual (table - get most common)
# +
# mean mutual per cell type (lowest = more change)
## plot interactions based on those genes (some are involved in more than one)
## heatmap - rows ct; columns genes; gaps between interact; 1 heatmap/cond, same ct ordering
sub_df1 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","ct1","mutInfo_he")])
sub_df2 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","ct2","mutInfo_he")])
ct_mut_he = tapply(c(sub_df1$mutInfo_he, sub_df2$mutInfo_he),
c(sub_df1$ct1, sub_df2$ct2), mean)
sub_df1 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","ct1","mutInfo_hr")])
sub_df2 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","ct2","mutInfo_hr")])
ct_mut_hr = tapply(c(sub_df1$mutInfo_hr, sub_df2$mutInfo_hr),
c(sub_df1$ct1, sub_df2$ct2), mean)
sub_df1 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","ct1","mutInfo_er")])
sub_df2 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","ct2","mutInfo_er")])
ct_mut_er = tapply(c(sub_df1$mutInfo_er, sub_df2$mutInfo_er),
c(sub_df1$ct1, sub_df2$ct2), mean)
ct_mut_df = cbind(ct_mut_he,ct_mut_hr,ct_mut_er)
rownames(ct_mut_df) = names(ct_mut_he)
colnames(ct_mut_df) = c("he", "hr", "er")
saveRDS(ct_mut_df, file = "./results/cell_comm/v3/ct_select_mutInfo_condComp.RDS")
sub_df1 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","lr1","mutInfo_he")])
sub_df2 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","lr2","mutInfo_he")])
lr_mut_he = tapply(c(sub_df1$mutInfo_he, sub_df2$mutInfo_he),
c(sub_df1$lr1, sub_df2$lr2), mean)
sub_df1 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","lr1","mutInfo_hr")])
sub_df2 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","lr2","mutInfo_hr")])
lr_mut_hr = tapply(c(sub_df1$mutInfo_hr, sub_df2$mutInfo_hr),
c(sub_df1$lr1, sub_df2$lr2), mean)
sub_df1 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","lr1","mutInfo_er")])
sub_df2 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","lr2","mutInfo_er")])
lr_mut_er = tapply(c(sub_df1$mutInfo_er, sub_df2$mutInfo_er),
c(sub_df1$lr1, sub_df2$lr2), mean)
lr_cnt_he = table(c(her_allint[her_allint$tot_he>0 & her_allint$mutInfo_he<=0.05,"lr1"],
her_allint[her_allint$tot_he>0 & her_allint$mutInfo_he<=0.05,"lr2"]))
lr_he = merge(lr_cnt_he, lr_mut_he, by.x = 1, by.y = 0)
lr_cnt_hr = table(c(her_allint[her_allint$tot_hr>0 & her_allint$mutInfo_hr<=0.05,"lr1"],
her_allint[her_allint$tot_hr>0 & her_allint$mutInfo_hr<=0.05,"lr2"]))
lr_hr = merge(lr_cnt_hr, lr_mut_hr, by.x = 1, by.y = 0)
lr_cnt_er = table(c(her_allint[her_allint$tot_er>0 & her_allint$mutInfo_er<=0.05,"lr1"],
her_allint[her_allint$tot_er>0 & her_allint$mutInfo_er<=0.05,"lr2"]))
lr_er = merge(lr_cnt_er, lr_mut_er, by.x = 1, by.y = 0)
# ECM - which proteins/collagens?; mention TGFB
# dev - which ligands/receptors
lr_all = rbind(lr_he, lr_hr, lr_er)
lr_all$cond = c(rep("he", nrow(lr_he)), rep("hr", nrow(lr_hr)),rep("er", nrow(lr_er)))
colnames(lr_all) = c("lr", "n_mut.05", "mean_mutInfo", "cond")
saveRDS(lr_all, file = paste0("./results/cell_comm/v3/", n, "LR_select_mutInfo_condComp.RDS"))
}Important tables
for(n in names(ct_int_exp_l)){
ct_int_exp = ct_int_exp_l[[n]]
r = if(n=="all") 1:3 else 4:6
# interactions
intdf = unique(Reduce(rbind, reform_list[r])[,c(1,4,5)])
intdf$intpair = paste0(intdf$lr1, " - ", intdf$lr2)
ct_int_exp_file = unique(ct_int_exp[,c(1,4:5,7:10)])
ct_int_exp_file$ctpair = paste0(ct_int_exp_file$ct, " - ", ct_int_exp_file$ct_target)
plot_df_int = reshape2::melt(ct_int_exp_file[,c(1,8,4:7)])
plot_df_int$variable = unlist(lapply(strsplit(as.character(plot_df_int$variable), "_"),
function(x) x[[1]][1]))
plot_df_int$variable = factor(plot_df_int$variable,
levels = rev(c("healthy", "embolised", "regenerating")))
sub_plot_df_int = plot_df_int[grepl("Stellate", plot_df_int$ctpair) |
grepl("Kupffer", plot_df_int$ctpair) |
grepl("LSEC", plot_df_int$ctpair),]
sub_plot_df_int = merge(sub_plot_df_int, intdf[,c(1,4)], by = 1, all.x = T)
gg = "ECM"
plt = ggplot(sub_plot_df_int[sub_plot_df_int$description==gg,],
aes(x = ctpair, y = variable, colour = value, size = value))+
facet_grid(intpair~.)+
guides(size = guide_legend(title = "exp", reverse = T),
colour = guide_legend(title = "exp", reverse = T))+
geom_point()+
labs(title = gg)+
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
strip.text.y = element_text(angle = 0, size = 8))
print(plt)
}Using interact, ctpair, description as id variables
Cell-cell communication networks
Load data to make cell comm networks (NOT USED HERE)
for(n in names(inter_df)){
write.csv(inter_df[[n]], col.names = T, row.names = F, quote = F,
file = paste0("results/cell_comm/v3/tables/Interact_", n, "_celltypes_cond.csv"))
}Warning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredfor(n in names(ct_int_exp_l)){
ct_int_exp = ct_int_exp_l[[n]]
write.csv(ct_int_exp, col.names = T, row.names = F, quote = F,
file = paste0("results/cell_comm/v3/tables/", n, "interact_celltype_exp_group.csv"))
}Warning: attempt to set 'col.names' ignoredWarning: attempt to set 'col.names' ignoredFunctions used to make cell comm networks
redone_meta = list()
for(cc in unique(allcells_css$Condition)){
redone_meta[[cc]] = read.table(paste0("results/cell_comm/CellPhoneDB_V3/",
cc, "/", cc, "_meta_names.txt"),
sep = "\t", header = T, row.names = 1)
}
redone_meta_all = Reduce(rbind, redone_meta)
allcells_redone = AddMetaData(allcells_css, redone_meta_all)
allcells_redone = allcells_redone[,!is.na(allcells_redone$cell_type)]Prepare data
makeMedian = function(point_df, edge_df, cl = c("ct2", "maj_g1", "maj_g2")){
mean_major = data.frame("X1" = tapply(point_df$X1, point_df[,cl[1]], median),
"X2" = tapply(point_df$X2, point_df[,cl[1]], median))
mean_major[,cl[1]] = rownames(mean_major)
edge_df[,cl[2]] = factor(edge_df[,cl[2]], levels = unique(c(edge_df[,cl[2]], edge_df[,cl[3]])))
edge_df[,cl[3]] = factor(edge_df[,cl[3]], levels = unique(c(as.character(edge_df[,cl[2]]),
edge_df[,cl[3]])))
maj_mat = table(edge_df[,cl[2]], edge_df[,cl[3]])
diag(maj_mat) = 0
edge_major = data.frame(maj_mat + t(maj_mat))
edge_major = merge(edge_major, mean_major, by.x = 1, by.y = 3)
edge_major = merge(edge_major, mean_major, by.x = 2, by.y = 3)
clcomb = combn(unique(c(as.character(edge_major$Var1), as.character(edge_major$Var2))), 2)
keep = c()
for(j in 1:ncol(clcomb)){
keep = c(keep, which(edge_major$Var2==clcomb[1,j] & edge_major$Var1==clcomb[2,j]))
}
edge_major = edge_major[keep,]
return(list(mean_major = mean_major, edge_major = edge_major))
}
makeMedianCond = function(point_df, edge_df, cl = c("ct", "ct_g1", "ct_g2"), edge_by = "condition"){
mean_major = data.frame("X1" = tapply(point_df$X1, point_df[,cl[1]], median),
"X2" = tapply(point_df$X2, point_df[,cl[1]], median))
mean_major[,cl[1]] = rownames(mean_major)
edge_df[,cl[2]] = factor(edge_df[,cl[2]], levels = unique(c(edge_df[,cl[2]], edge_df[,cl[3]])))
edge_df[,cl[3]] = factor(edge_df[,cl[3]], levels = unique(c(as.character(edge_df[,cl[2]]),
edge_df[,cl[3]])))
edge_l = list()
for(i in unique(edge_df[,edge_by])){
sub_edge_df = edge_df[edge_df[,edge_by]==i,]
maj_mat = table(sub_edge_df[,cl[2]], sub_edge_df[,cl[3]])
diag(maj_mat) = 0
edge_major = data.frame(maj_mat + t(maj_mat))
edge_major = merge(edge_major, mean_major, by.x = 1, by.y = 3)
edge_major = merge(edge_major, mean_major, by.x = 2, by.y = 3)
# remove repeated
clcomb = combn(unique(c(as.character(edge_major$Var1), as.character(edge_major$Var2))), 2)
keep = c()
for(j in 1:ncol(clcomb)){
keep = c(keep, which(edge_major$Var2==clcomb[1,j] & edge_major$Var1==clcomb[2,j]))
}
edge_l[[i]] = edge_major[keep,]
}
edge_major = Reduce(rbind, edge_l)
edge_major[,edge_by] = unlist(lapply(names(edge_l), function(x) rep(x, nrow(edge_l[[x]]))))
edge_major = edge_major[edge_major$Var2!=edge_major$Var1,]
return(list(mean_major = mean_major, edge_major = edge_major))
}Plot ligands and receptors with MDS
inter_df = readRDS(file = "results/cell_comm/v3/cond_diff_interact_DE.RDS")
# interactions unique to each condition
unique_inters = c(setdiff(inter_df$healthy$id_cp_interaction,
c(inter_df$embolised$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
setdiff(inter_df$embolised$id_cp_interaction,
c(inter_df$healthy$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
setdiff(inter_df$regenerating$id_cp_interaction,
c(inter_df$embolised$id_cp_interaction, inter_df$healthy$id_cp_interaction)))
# interactions unique to healthy or to emb/regen
comph_inters = c(setdiff(inter_df$healthy$id_cp_interaction,
c(inter_df$embolised$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
setdiff(inter_df$embolised$id_cp_interaction, inter_df$healthy$id_cp_interaction),
setdiff(inter_df$regenerating$id_cp_interaction, inter_df$healthy$id_cp_interaction))
# prepare gene pairs per condition
gene_pairs_cond = rbind(unique(inter_df$healthy[,c("id_cp_interaction", "gn1", "gn2")]),
unique(inter_df$embolised[,c("id_cp_interaction", "gn1", "gn2")]),
unique(inter_df$regenerating[,c("id_cp_interaction", "gn1", "gn2")]))
gene_pairs_cond$condition = c(rep("healthy", nrow(unique(inter_df$healthy[,c("id_cp_interaction",
"gn1", "gn2")]))),
rep("embolised", nrow(unique(inter_df$embolised[,c("id_cp_interaction",
"gn1", "gn2")]))),
rep("regenerating", nrow(unique(inter_df$regenerating[,c("id_cp_interaction",
"gn1", "gn2")]))))
# list all LR genes
all_lr_genes = unique(c(as.character(inter_df$healthy$gn1), as.character(inter_df$healthy$gn2),
as.character(inter_df$embolised$gn1), as.character(inter_df$embolised$gn2),
as.character(inter_df$regenerating$gn1), as.character(inter_df$regenerating$gn2)))
all_lr_genes = all_lr_genes[all_lr_genes %in% rownames(sub_allcells_css@assays$SCT@data)]
# calculate mean per cell type and condition for each LR gene
mean_exp_cond_lr = apply(sub_allcells_css@assays$SCT@data[all_lr_genes,], 1,
function(x) tapply(x, paste0(sub_allcells_css$subpops,
"_", sub_allcells_css$Condition), mean))
# determine the cell type and condition with the highest expression
max_cond_ct = rownames(mean_exp_cond_lr)[apply(mean_exp_cond_lr, 2, which.max)]
names(max_cond_ct) = colnames(mean_exp_cond_lr)
max_cond_ct_ct = unlist(lapply(strsplit(max_cond_ct, "_"), function(x) x[1]))
max_cond_ct_cond = unlist(lapply(strsplit(max_cond_ct, "_"), function(x) x[length(x)]))
# correlation of mean expression
cor_cond_lr = cor(mean_exp_cond_lr, method = "sp")
# filter correlation with itself, keep only genes with cor>=0.3
diag(cor_cond_lr) = 0
adj_cond_mat = cor_cond_lr>=0.3MDS with simplified labels
# build graph, project with MDS
network_cond = graph_from_adjacency_matrix(adj_cond_mat, weighted=T, mode="undirected", diag=F)
l_cond = igraph::layout_with_mds(network_cond)
l_cond = data.frame(l_cond)
l_cond$gene = colnames(adj_cond_mat)
rownames(l_cond) = colnames(adj_cond_mat)
# define all edges, based on CellPhoneDB pairings
tmp_df = merge(gene_pairs_cond, l_cond, by.x = "gn1", by.y = "gene")
edge_cond_df = merge(tmp_df, l_cond, by.x = "gn2", by.y = "gene")
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_ct), by.x = 1, by.y = 0, all.x = T)
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_ct), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_df)[9:10] = c("ct_g1", "ct_g2")
# add highest expressing major cell types
edge_cond_df$maj_g1 = ifelse(grepl("LSEC", edge_cond_df$ct_g1), "Endothelial",
ifelse(grepl("Hepatocytes", edge_cond_df$ct_g1), "Hepatocytes",
ifelse(grepl("Stellate cells", edge_cond_df$ct_g1) |
grepl("Fibroblast", edge_cond_df$ct_g1) |
grepl("VSMC", edge_cond_df$ct_g1), "Mesenchymal",
ifelse(grepl("Cholangiocytes", edge_cond_df$ct_g1), "Cholangiocytes",
"Immune"))))
edge_cond_df$maj_g2 = ifelse(grepl("LSEC", edge_cond_df$ct_g2), "Endothelial",
ifelse(grepl("Hepatocytes", edge_cond_df$ct_g2), "Hepatocytes",
ifelse(grepl("Stellate cells", edge_cond_df$ct_g2) |
grepl("Fibroblast", edge_cond_df$ct_g2) |
grepl("VSMC", edge_cond_df$ct_g2), "Mesenchymal",
ifelse(grepl("Cholangiocytes", edge_cond_df$ct_g2), "Cholangiocytes",
"Immune"))))
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_cond), by.x = 1, by.y = 0, all.x = T)
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_cond), by.x = 2, by.y = 0, all.x = T)
# define the vertices of the network
point_cond_df = l_cond
point_cond_df$ct = max_cond_ct_ct[point_cond_df$gene]
point_cond_df$ct2 = ifelse(grepl("LSEC", point_cond_df$ct), "Endothelial",
ifelse(grepl("Hepatocytes", point_cond_df$ct), "Hepatocytes",
ifelse(grepl("Stellate cells", point_cond_df$ct) |
grepl("VSMC", point_cond_df$ct) |
grepl("Fibroblast", point_cond_df$ct), "Mesenchymal",
ifelse(grepl("Cholangiocytes", point_cond_df$ct), "Cholangiocytes",
"Immune"))))
point_cond_df$cond = max_cond_ct_cond[point_cond_df$gene]
# define the median points for each cell type (using max expression)
pe_l = makeMedian(point_cond_df, edge_cond_df, cl = c("ct", "ct_g1", "ct_g2"))
# plot total gene correlation projection and median network
pltboth = ggplot()+
geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.25, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_segment(data = pe_l[[2]],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq),
alpha = 0.15, show.legend = F)+
geom_point(data = pe_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
pltboth = ggplot()+
geom_segment(data = edge_cond_df,
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y),
alpha = 0.03, show.legend = F)+
geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.6, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_point(data = pe_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
# get median per cell type, per condition - FULL NETWORK
pe_cond_l = makeMedianCond(point_cond_df, edge_cond_df, cl = c("ct", "ct_g1", "ct_g2"))
plt_cond_l = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
plt_cond_l[[cc]] = ggplot()+
geom_segment(data = pe_cond_l[[2]][pe_cond_l[[2]]$condition==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y,
size = Freq, alpha = Freq))+
geom_point(data = pe_cond_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_cond_l[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_cond_l[["leg"]] = cowplot::get_legend(plt_cond_l[["healthy"]])
# plot FULL NETWORK median per condition
cowplot::plot_grid(plt_cond_l[[1]]+theme(legend.position = "none"),
plt_cond_l[[2]]+theme(legend.position = "none"),
plt_cond_l[[3]]+theme(legend.position = "none"), plt_cond_l$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
# get median per cell type, per condition - UNIQUE PER CONDTION NETWORK
pe_cond_l_u = makeMedianCond(point_cond_df, edge_cond_df[edge_cond_df$id_cp_interaction %in% unique_inters,],
cl = c("ct", "ct_g1", "ct_g2"))
plt_cond_l_u = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
plt_cond_l_u[[cc]] = ggplot()+
geom_segment(data = pe_cond_l_u[[2]][pe_cond_l_u[[2]]$condition==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_cond_l_u[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_u[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_cond_l_u[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_cond_l_u[["leg"]] = cowplot::get_legend(plt_cond_l_u[["healthy"]])
# plot UNIQUE PER CONDTION NETWORK median per condition
cowplot::plot_grid(plt_cond_l_u[[1]]+theme(legend.position = "none"),
plt_cond_l_u[[2]]+theme(legend.position = "none"),
plt_cond_l_u[[3]]+theme(legend.position = "none"), plt_cond_l_u$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
# get median per cell type, per condition - HEALTHY NETWORK
pe_cond_l_h = makeMedianCond(point_cond_df, edge_cond_df[edge_cond_df$id_cp_interaction %in% inter_df$healthy$id_cp_interaction,], cl = c("ct", "ct_g1", "ct_g2"))
plt_cond_l_h = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
plt_cond_l_h[[cc]] = ggplot()+
geom_segment(data = pe_cond_l_h[[2]][pe_cond_l_h[[2]]$condition==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_cond_l_h[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_h[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_cond_l_h[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_cond_l_h[["leg"]] = cowplot::get_legend(plt_cond_l_h[["healthy"]])
# plot HEALTHY NETWORK median per condition
cowplot::plot_grid(plt_cond_l_h[[1]]+theme(legend.position = "none"),
plt_cond_l_h[[2]]+theme(legend.position = "none"),
plt_cond_l_h[[3]]+theme(legend.position = "none"), plt_cond_l_h$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_cond_l_ch = makeMedianCond(point_cond_df,
edge_cond_df[edge_cond_df$id_cp_interaction %in% comph_inters,],
cl = c("ct", "ct_g1", "ct_g2"))
plt_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
plt_cond_l_ch[[cc]] = ggplot()+
geom_segment(data = pe_cond_l_ch[[2]][pe_cond_l_ch[[2]]$condition==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_cond_l_ch[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_ch[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_cond_l_ch[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_cond_l_ch[["leg"]] = cowplot::get_legend(plt_cond_l_ch[["healthy"]])
# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_cond_l_ch[[1]]+theme(legend.position = "none"),
plt_cond_l_ch[[2]]+theme(legend.position = "none"),
plt_cond_l_ch[[3]]+theme(legend.position = "none"), plt_cond_l_ch$leg,
ncol = 2, rel_widths = c(1,1,1,0.5))
Save network objects
# add labels to points
point_cond_df$ct_mid = point_cond_df$ct
point_cond_df$ct_mid[grepl("NK", point_cond_df$ct)] = "ILC"
point_cond_df$ct_mid[grepl("ILC", point_cond_df$ct)] = "ILC"
point_cond_df$ct_mid[grepl("T cells", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("MAIT", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("Treg", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("Stellate", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("Fibroblast", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("VSMC", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("LSEC", point_cond_df$ct)] = "LSEC"
point_cond_df$ct_mid[grepl("non-LSEC", point_cond_df$ct,
fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Lymphatic EC", point_cond_df$ct,
fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Dividing endothelial cells", point_cond_df$ct,
fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Monocytes", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("Macrophages", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("cDC", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("activated DCs", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("Kupffer", point_cond_df$ct)] = "Kupffer cells"
point_cond_df$ct_mid[grepl("Plasma", point_cond_df$ct)] = "B cells"
# add labels to edges
match_df = unique(data.frame("ct" = point_cond_df$ct,
"ct_mid" = point_cond_df$ct_mid))
rownames(match_df) = match_df$ct
edge_cond_df$mid_g1 = match_df[edge_cond_df$ct_g1,"ct_mid"]
edge_cond_df$mid_g2 = match_df[edge_cond_df$ct_g2,"ct_mid"]
pe_mid_l = makeMedian(point_cond_df, edge_cond_df,
cl = c("ct_mid", "mid_g1", "mid_g2"))
# plot total gene correlation projection and median network
pltboth = ggplot()+
geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.25, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_segment(data = pe_mid_l[[2]],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq),
alpha = 0.15, show.legend = F)+
geom_point(data = pe_mid_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct_mid),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_cond_l_mid = makeMedianCond(point_cond_df,
edge_cond_df[edge_cond_df$id_cp_interaction%in%comph_inters,],
edge_by = "condition",
cl = c("ct_mid", "mid_g1", "mid_g2"))
plt_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
plt_cond_l_ch[[cc]] = ggplot()+
geom_segment(data = pe_cond_l_mid[[2]][pe_cond_l_mid[[2]]$condition==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_cond_l_mid[[1]],
mapping = aes(x = X1, y = X2, fill = ct_mid),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_mid[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_cond_l_mid[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_cond_l_ch[["leg"]] = cowplot::get_legend(plt_cond_l_ch[["healthy"]])
# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_cond_l_ch[[1]]+theme(legend.position = "none"),
plt_cond_l_ch[[2]]+theme(legend.position = "none"),
plt_cond_l_ch[[3]]+theme(legend.position = "none"), plt_cond_l_ch$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
Plot ligands and receptors with UMAP
saveRDS(comph_inters, file = "results/cell_comm/v3/comph_inters.RDS")
save(edge_cond_df, point_cond_df, file = "results/cell_comm/v3/networks_cond.RData")
save(pe_l, pe_cond_l, pe_cond_l_u, pe_cond_l_h, pe_cond_l_ch,
file = "results/cell_comm/v3/median_networks_cond.RData")
save(pe_mid_l, pe_cond_l_mid,
file = "results/cell_comm/v3/median_networks_cond_midct.RData")UMAPs with simplified labels
set.seed(2954)
l = uwot::umap(t(mean_exp_cond_lr), metric = "cosine", ret_nn = T, n_epochs = 1000)
l_cond = data.frame(l$embedding)
l_cond$gene = colnames(mean_exp_cond_lr)
rownames(l_cond) = colnames(mean_exp_cond_lr)
tmp_df = merge(gene_pairs_cond, l_cond, by.x = "gn1", by.y = "gene")
edge_cond_umap_df = merge(tmp_df, l_cond, by.x = "gn2", by.y = "gene")
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_ct), by.x = 1, by.y = 0, all.x = T)
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_ct), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_umap_df)[9:10] = c("ct_g1", "ct_g2")
edge_cond_umap_df$maj_g1 = ifelse(grepl("LSEC", edge_cond_umap_df$ct_g1), "Endothelial",
ifelse(grepl("Hepatocytes", edge_cond_umap_df$ct_g1), "Hepatocytes",
ifelse(grepl("Stellate cells", edge_cond_umap_df$ct_g1) |
grepl("VSMC", edge_cond_umap_df$ct_g1) |
grepl("Fibroblast", edge_cond_umap_df$ct_g1), "Mesenchymal",
ifelse(grepl("Cholangiocytes", edge_cond_umap_df$ct_g1),
"Cholangiocytes", "Immune"))))
edge_cond_umap_df$maj_g2 = ifelse(grepl("LSEC", edge_cond_umap_df$ct_g2), "Endothelial",
ifelse(grepl("Hepatocytes", edge_cond_umap_df$ct_g2), "Hepatocytes",
ifelse(grepl("Stellate cells", edge_cond_umap_df$ct_g2) |
grepl("VSMC", edge_cond_umap_df$ct_g2) |
grepl("Fibroblast", edge_cond_umap_df$ct_g2), "Mesenchymal",
ifelse(grepl("Cholangiocytes", edge_cond_umap_df$ct_g2),
"Cholangiocytes", "Immune"))))
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_cond), by.x = 1, by.y = 0, all.x = T)
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_cond), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_umap_df)[4] = "cond"
point_cond_umap_df = l_cond
point_cond_umap_df$ct = max_cond_ct_ct[point_cond_umap_df$gene]
point_cond_umap_df$ct2 = ifelse(grepl("LSEC", point_cond_umap_df$ct), "Endothelial",
ifelse(grepl("Hepatocytes", point_cond_umap_df$ct), "Hepatocytes",
ifelse(grepl("Stellate cells", point_cond_umap_df$ct) |
grepl("VSMC", point_cond_umap_df$ct) |
grepl("Fibroblast", point_cond_umap_df$ct), "Mesenchymal",
ifelse(grepl("Cholangiocytes", point_cond_umap_df$ct), "Cholangiocytes",
"Immune"))))
point_cond_umap_df$cond = max_cond_ct_cond[point_cond_umap_df$gene]
pe_umap_l = makeMedian(point_cond_umap_df, edge_cond_umap_df, cl = c("ct", "ct_g1", "ct_g2"))
# plot total gene correlation projection and median network
pltboth = ggplot()+
geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.25, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_segment(data = pe_umap_l[[2]],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq),
alpha = 0.15, show.legend = F)+
geom_point(data = pe_umap_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
pltboth = ggplot()+
geom_segment(data = edge_cond_umap_df,
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y),
alpha = 0.03, show.legend = F)+
geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.6, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_point(data = pe_umap_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_umap_cond_l_ch = makeMedianCond(point_cond_umap_df,
edge_cond_umap_df[edge_cond_umap_df$id_cp_interaction%in%comph_inters,],
edge_by = "cond", cl = c("ct", "ct_g1", "ct_g2"))
plt_umap_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$cond)){
plt_umap_cond_l_ch[[cc]] = ggplot()+
geom_segment(data = pe_umap_cond_l_ch[[2]][pe_umap_cond_l_ch[[2]]$cond==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_umap_cond_l_ch[[1]],
mapping = aes(x = X1, y = X2, fill = ct),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_umap_cond_l_ch[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_umap_cond_l_ch[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_umap_cond_l_ch[["leg"]] = cowplot::get_legend(plt_umap_cond_l_ch[["healthy"]])
# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_umap_cond_l_ch[[1]]+theme(legend.position = "none"),
plt_umap_cond_l_ch[[2]]+theme(legend.position = "none"),
plt_umap_cond_l_ch[[3]]+theme(legend.position = "none"), plt_umap_cond_l_ch$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
Save UMAP network objects
# add labels to points
point_cond_umap_df$ct_mid = point_cond_umap_df$ct
point_cond_umap_df$ct_mid[grepl("NK", point_cond_umap_df$ct)] = "ILC"
point_cond_umap_df$ct_mid[grepl("ILC", point_cond_umap_df$ct)] = "ILC"
point_cond_umap_df$ct_mid[grepl("T cells", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("MAIT", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("Treg", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("Stellate", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("VSMC", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("Fibroblast", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("LSEC", point_cond_umap_df$ct)] = "LSEC"
point_cond_umap_df$ct_mid[grepl("non-LSEC", point_cond_umap_df$ct,
fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Lymphatic EC", point_cond_umap_df$ct,
fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Dividing endothelial cells", point_cond_umap_df$ct,
fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Monocytes", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("Macrophages", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("cDC", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("activated DCs", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("Kupffer", point_cond_umap_df$ct)] = "Kupffer cells"
point_cond_umap_df$ct_mid[grepl("Plasma", point_cond_umap_df$ct)] = "B cells"
# add labels to edges
match_df = unique(data.frame("ct" = point_cond_umap_df$ct,
"ct_mid" = point_cond_umap_df$ct_mid))
rownames(match_df) = match_df$ct
edge_cond_umap_df$mid_g1 = match_df[edge_cond_umap_df$ct_g1,"ct_mid"]
edge_cond_umap_df$mid_g2 = match_df[edge_cond_umap_df$ct_g2,"ct_mid"]
pe_umap_mid_l = makeMedian(point_cond_umap_df, edge_cond_umap_df,
cl = c("ct_mid", "mid_g1", "mid_g2"))
# plot total gene correlation projection and median network
pltboth = ggplot()+
geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2),
alpha = 0.25, show.legend = F)+
scale_shape_manual(values = c(0,4,19))+
geom_segment(data = pe_umap_mid_l[[2]],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq),
alpha = 0.15, show.legend = F)+
geom_point(data = pe_umap_mid_l[[1]],
mapping = aes(x = X1, y = X2, fill = ct_mid),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4))+
theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)
# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_umap_cond_l_mid = makeMedianCond(point_cond_umap_df,
edge_cond_umap_df[edge_cond_umap_df$id_cp_interaction%in%comph_inters,],
edge_by = "cond", cl = c("ct_mid", "mid_g1", "mid_g2"))
plt_umap_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$cond)){
plt_umap_cond_l_ch[[cc]] = ggplot()+
geom_segment(data = pe_umap_cond_l_mid[[2]][pe_umap_cond_l_mid[[2]]$cond==cc,],
mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
geom_point(data = pe_umap_cond_l_mid[[1]],
mapping = aes(x = X1, y = X2, fill = ct_mid),
alpha = 1, pch = 21, size = 4)+
scale_size_continuous(range = c(0, 4), limits = range(pe_umap_cond_l_mid[[2]]$Freq))+
scale_alpha_continuous(limits = range(pe_umap_cond_l_mid[[2]]$Freq))+
labs(title = cc)+
guides(size = guide_legend(direction = "horizontal", nrow = 2),
alpha = guide_legend(direction = "horizontal", nrow = 2))+
theme_classic()
}
plt_umap_cond_l_ch[["leg"]] = cowplot::get_legend(plt_umap_cond_l_ch[["healthy"]])
# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_umap_cond_l_ch[[1]]+theme(legend.position = "none"),
plt_umap_cond_l_ch[[2]]+theme(legend.position = "none"),
plt_umap_cond_l_ch[[3]]+theme(legend.position = "none"), plt_umap_cond_l_ch$leg,
ncol = 4, rel_widths = c(1,1,1,0.5))
---
title: "Cell communication analysis"
output: html_notebook
---



# General Setup
Setup chunk

```{r, setup, include=FALSE}
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")
```

Setup reticulate

```{r}
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)
use_python(Sys.which("python"))
py_config()
```

Load libraries

```{r}
library(Seurat)
library(ggplot2)
library(ggridges)
library(dplyr)
library(igraph)
library(data.table)
```


# Load and preprocess data
Load data (from all cells)

```{r}
allcells_css = readRDS(file = "data/processed/allcells_css.RDS")
end_cells = readRDS(file = "results/endothelial/only_end_cells_zon.RDS")
hep_cells = readRDS(file = "results/zonation_cond/hep_cells_zonation_rank.RDS")
imm_cells = readRDS(file = "results/immune/all_imm_cells.RDS")

# mesenchymal annotation
mes_annot = read.csv("./data/processed/mesenchymal_fresh_annot.csv", 
                     row.names = 1, header = T, )
```

Prepare a global object with all necessary metadata

```{r}
# endothelial
endpop_df = data.frame(row.names = colnames(end_cells),
                       "subpops" = end_cells@meta.data$endo_simp)
# immune
immpop_df = data.frame(row.names = colnames(imm_cells),
                       "subpops" = imm_cells@meta.data$immune_annot)
# hepatocyte
heppop_df = lapply(hep_cells, function(x) cbind(colnames(x), as.character(x$zonation_int)))
heppop_df = Reduce(rbind, heppop_df)
heppop_df = data.frame(row.names = heppop_df[,1],
                       subpops = factor(heppop_df[,2]))
levels(heppop_df$subpops) = c("(-0.00099,0.333]" = "Hepatocytes_Z1",
                              "(0.333,0.667]" = "Hepatocytes_Z2",
                              "(0.667,1]" = "Hepatocytes_Z3")
heppop_df$subpops = as.character(heppop_df$subpops)
# mesenchymal
mes_df = mes_annot[,"annotation", drop = F]
colnames(mes_df) = "subpops"
mes_df$subpops[grepl("doublets",mes_df$subpops)] = "Doublets"

subpop_df = rbind(endpop_df, immpop_df, heppop_df, mes_df)
subpop_df$subpops[subpop_df$subpops=="Cycling cells"] = "Dividing endothelial cells"

# add subpop metadata
allcells_css = AddMetaData(allcells_css, subpop_df)
allcells_css$subpops[is.na(allcells_css$subpops)] = allcells_css$allcells_major[is.na(allcells_css$subpops)]
allcells_css$subpops[allcells_css$subpops=="Hepatocyte-Monocyte interaction"] = "Doublets"

# the cells in this object named "Hepatocytes", "Endothelial cells", and "Doublets" have to be removed
## the first two are cells that didn't pass the hep and end analysis
sub_allcells_css = allcells_css[,!(allcells_css$subpops %in% c("Hepatocytes", "Endothelial cells",
                                                               "Doublets"))]

# add simplified column - merge some populations, don't include cycling pops and stressed T cells
sub_allcells_css$subpops_simp = sub_allcells_css$subpops
sub_allcells_css$subpops_simp[grepl("MAIT", sub_allcells_css$subpops_simp)] = "MAIT cells"
sub_allcells_css$subpops_simp[grepl("NK cells ", sub_allcells_css$subpops_simp)] = "NK cells"
sub_allcells_css$subpops_simp[grepl("LSEC (high MT", sub_allcells_css$subpops_simp, 
                                    fixed = T)] = "LSEC (high MT)"
sub_allcells_css$subpops_simp[grepl("CD8 ab-T ", sub_allcells_css$subpops_simp, 
                                    fixed = T)] = "CD8 ab-T cells"
simp_allcells_css = sub_allcells_css[,!(sub_allcells_css$subpops_simp %in% c("ab-T cells (stress)", "Dividing cDCs", "Dividing endothelial cells","Dividing T/NK cells"))]
```

Subset and process each condition

```{r}
#cpdb_path = "results/cell_comm/CellPhoneDB_V3"
cpdb_path = "/local1/scratch/tomasgomes/CellPhoneDB_V3"

cond_cells = list()
for(cond in unique(sub_allcells_css@meta.data$Condition)){
  if(!dir.exists(paste0(cpdb_path, "/", cond, "/"))){
    dir.create(paste0(cpdb_path, "/", cond, "/"))
  }
  # subset condition
  cond_cells[[cond]] = sub_allcells_css[,sub_allcells_css@meta.data$Condition==cond]
  
  # define metadata
  meta = data.frame(row.names = rownames(cond_cells[[cond]]@meta.data),
                    "Cell" = rownames(cond_cells[[cond]]@meta.data), 
                    "cell_type" = as.character(cond_cells[[cond]]@meta.data$subpops),
                    stringsAsFactors = F)
  write.table(meta, file = paste0(cpdb_path, "/", cond, "/", cond, "_meta_names.txt"), 
            sep = "\t", col.names = T, row.names = F, quote = F)
  
  # normalise and save
  cond_cells[[cond]] = suppressWarnings(SCTransform(cond_cells[[cond]], do.correct.umi = T, 
                                                    vars.to.regress=c("unique_name", "nCount_RNA"),
                                                    variable.features.rv.th = 1, seed.use = 1,
                                                    return.only.var.genes = F, verbose = F,
                                                    variable.features.n = NULL))
  
  dat = cbind(rownames(cond_cells[[cond]]@assays$SCT@data),
              Matrix::as.matrix(cond_cells[[cond]]@assays$SCT@data))
  colnames(dat)[1] = "Gene"
  write.table(dat, file = paste0(cpdb_path, "/", cond, "/", cond, "_exp_norm.txt"), 
              sep = "\t", col.names = T, row.names = F, quote = F)
  
  
  if(!dir.exists(paste0(cpdb_path, "/", cond, "_simp/"))){
    dir.create(paste0(cpdb_path, "/", cond, "_simp/"))
  }
  # subset condition
  cond_cells[[cond]] = simp_allcells_css[,simp_allcells_css@meta.data$Condition==cond]
  
  # define metadata
  meta_simp = data.frame(row.names = rownames(cond_cells[[cond]]@meta.data),
                         "Cell" = rownames(cond_cells[[cond]]@meta.data), 
                         "cell_type" = as.character(cond_cells[[cond]]@meta.data$subpops_simp),
                         stringsAsFactors = F)
  write.table(meta_simp, file = paste0(cpdb_path, "/", cond, "_simp/", cond, "_meta_names.txt"), 
            sep = "\t", col.names = T, row.names = F, quote = F)
   
  # normalise and save
  cond_cells[[cond]] = suppressWarnings(SCTransform(cond_cells[[cond]], do.correct.umi = T, 
                                                    vars.to.regress=c("unique_name", "nCount_RNA"),
                                                    variable.features.rv.th = 1, seed.use = 1,
                                                    return.only.var.genes = F, verbose = F,
                                                    variable.features.n = NULL))
  
  dat = cbind(rownames(cond_cells[[cond]]@assays$SCT@data),
              Matrix::as.matrix(cond_cells[[cond]]@assays$SCT@data))
  colnames(dat)[1] = "Gene"
  write.table(dat, file = paste0(cpdb_path, "/", cond, "_simp/", cond, "_exp_norm.txt"), 
              sep = "\t", col.names = T, row.names = F, quote = F)
}
```


## Running CellPhoneDB
Commands for runnin CellPhoneDB will be written here. Results are output to the `local1` disk to avoid I/O issues, but the output folders should then be copied to: `results/cell_comm_healthy/CellPhoneDB_runs`.  
NOTE: for some reason I'm getting a SegFault when trying to run the heatmap_plot command. This was run on the Euler server instead.

```{r}
for(n in names(cond_cells)){
  comm1 = paste0("cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, " --project-name ", n, " --counts-data gene_name")
  comm2 = paste0("cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "/", n, " results/cell_comm/CellPhoneDB_V3/")
  comm3 = paste0("cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt results/cell_comm/CellPhoneDB_V3/", n, "/")
  comm4 = paste0("cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/", n, "/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/", n, "/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt results/cell_comm/CellPhoneDB_V3/", n, "/", n, "_meta_names.txt")
  
  print(n)
  print(comm1)
  print(comm2)
  print(comm3)
  print(comm4)
  print(".")
}
```

```{r}
for(n in names(cond_cells)){
  comm1 = paste0("cellphonedb method statistical_analysis /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_exp_norm.txt --threads=12 --output-path=/local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "_simp --project-name ", n, "_simp --counts-data gene_name")
  comm2 = paste0("cp -r /local1/scratch/tomasgomes/liver/CellPhoneDB_V3/", n, "_simp/", n, "_simp results/cell_comm/CellPhoneDB_V3/")
  comm3 = paste0("cp -r /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt results/cell_comm/CellPhoneDB_V3/", n, "_simp/")
  comm4 = paste0("cellphonedb plot heatmap_plot --pvalues-path ./results/cell_comm/CellPhoneDB_V3/", n, "_simp/pvalues.txt --output-path ./results/cell_comm/CellPhoneDB_V3/", n, "_simp/ --count-name counts_heat.pdf --count-network-name count_net.txt --interaction-count-name count_inter.txt /local1/scratch/tomasgomes/CellPhoneDB_V3/", n, "_simp/", n, "_meta_names.txt")
  
  print(n)
  print(comm1)
  print(comm2)
  print(comm3)
  print(comm4)
  print(".")
}
```



# Process results
Load results

```{r}
cpdb_path = "results/cell_comm/CellPhoneDB_V3"

sig_means_names_l = list()
dec_l = list()
net_names_l = list()
meta_l = list()
conds = unique(allcells_css@meta.data$Condition)
for(n in c(conds, paste0(conds, "_simp"))){
  ns = strsplit(n, "_")[[1]][1]
  sig_means_names_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/significant_means.txt"),
                                      header = T, sep = "\t", stringsAsFactors = F)
  dec_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/deconvoluted.txt"),
                          header = T, sep = "\t")
  colnames(dec_l[[n]]) = gsub(".", " ", colnames(dec_l[[n]]), fixed = T)
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="gd T cells"] = "gd-T cells"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Macrophages  HES4  "] = "Macrophages (HES4+)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells"] = "CD8 ab-T cells"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 1"] = "CD8 ab-T cells 1"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 2"] = "CD8 ab-T cells 2"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells 3"] = "CD8 ab-T cells 3"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Naive CD4  T cells"] = "Naive CD4+ T cells"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="ab T cells  stress "] = "ab-T cells (stress)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes  secretory "] = "Monocytes (secretory)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes  TREM2  CD9  "] = "Monocytes (TREM2+ CD9+)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Monocytes  IGSF21  GPR34  "] = "Monocytes (IGSF21+ GPR34+)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  stress "] = "LSEC (stress)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  remodelling "] = "LSEC (remodelling)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  interferon "] = "LSEC (interferon)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  high MT 2 "] = "LSEC (high MT 2)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  high MT 1 "] = "LSEC (high MT 1)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  high MT "] = "LSEC (high MT)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="LSEC  fenestr  "] = "LSEC (fenestr.)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Kupffer cells  SUCNR1  "] = "Kupffer cells (SUCNR1+)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="IgG  Plasma cells"] = "IgG+ Plasma cells"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="IgA  Plasma cells"] = "IgA+ Plasma cells"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="CD8 ab T cells  stress "] = "CD8 ab-T cells (stress)"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="EC non LSEC"] = "EC non-LSEC"
  colnames(dec_l[[n]])[colnames(dec_l[[n]])=="Dividing T NK cells"] = "Dividing T/NK cells"
  net_names_l[[n]] = read.table(paste0(cpdb_path, "/", n, "/count_net.txt"),
                                header = T, sep = "\t", stringsAsFactors = F)
  meta_l[[n]] = if(!grepl("simp", n)){
    read.table(paste0(cpdb_path, "/", n, "/", n, "_meta_names.txt"),header = T,sep = "\t")
  } else{
    read.table(paste0(cpdb_path, "/", n, "/", ns, "_meta_names.txt"),header = T,sep = "\t")
  }
}
saveRDS(dec_l, file = "results/cell_comm/v3/deconvoluted_list.RDS")
saveRDS(net_names_l, file = "results/cell_comm/v3/count_net_list.RDS")
```

Reformat significant means matrix

```{r}
reform_list = list()
for(n in names(meta_l)){
  simp_reform = reshape2::melt(sig_means_names_l[[n]][,c(1:4,7:9,13:ncol(sig_means_names_l[[n]]))])
  simp_reform = simp_reform[complete.cases(simp_reform),]
  
  lr1 = c()
  lr2 = c()
  for(i in 1:nrow(simp_reform)){
    if(grepl("complex", simp_reform$partner_a[i])){
      lr1 = c(lr1, substr(simp_reform$partner_a[i], 9,100))
    } else{
      lr1 = c(lr1, strsplit(simp_reform$interacting_pair[i], "_")[[1]][1])
    }
    if(grepl("complex", simp_reform$partner_b[i])){
      lr2 = c(lr2, substr(simp_reform$partner_b[i], 9,100))
    } else{
      spltlen = length(strsplit(simp_reform$interacting_pair[i], "_")[[1]])
      lr2 = c(lr2, strsplit(simp_reform$interacting_pair[i], "_")[[1]][spltlen])
    }
  }
  simp_reform$lr1 = lr1
  simp_reform$lr2 = lr2
  
  simp_reform$ct1 = NA
  simp_reform$ct2 = NA
  donest = rep(NA, length(simp_reform$ct1))
  doneen = rep(NA, length(simp_reform$ct2))
  for(ct in sort(unique(meta_l[[n]]$cell_type), decreasing = T)){
    ct_mod = gsub("-", " ", ct, fixed = T)
    ct_mod = gsub("+", " ", ct_mod, fixed = T)
    ct_mod = gsub("/", " ", ct_mod, fixed = T)
    ct_mod = gsub("(", " ", ct_mod, fixed = T)
    ct_mod = gsub(")", " ", ct_mod, fixed = T)
    ct_mod = gsub(".", " ", ct_mod, fixed = T)
    st = grepl(paste0("^",ct_mod, " "), gsub(".", " ", simp_reform$variable, fixed = T), fixed = F)
    en = grepl(paste0(" ",ct_mod,"$"), gsub(".", " ", simp_reform$variable, fixed = T), fixed = F)
    simp_reform$ct1[st & is.na(donest)] = ct
    simp_reform$ct2[en & is.na(doneen)] = ct
    donest[st] = T
    doneen[en] = T
  }
  #colnames(dec_l[[n]])[!(colnames(dec_l[[n]]) %in% unique(simp_reform$ct2))]
  
  # direction: receptors activate internal signal - that is the direction of the signalling
  ## if both are true, the "net signal" is 0
  simp_reform$dir = ifelse(simp_reform$receptor_a==simp_reform$receptor_b, 0, 
                           ifelse(simp_reform$receptor_a=="True" & 
                                    simp_reform$receptor_b=="False", -1,
                                  ifelse(simp_reform$receptor_a=="False" &
                                           simp_reform$receptor_b=="True", 1, NA)))
  
  simp_reform = simp_reform[,c("id_cp_interaction", "ct1", "ct2", "lr1", "lr2", 
                               "value", "dir")]
  
  for(i in 1:nrow(simp_reform)){
    if(simp_reform[i,"dir"]==-1){
      tmp = simp_reform[i,"ct2"]
      simp_reform[i,"ct2"] = simp_reform[i,"ct1"]
      simp_reform[i,"ct1"] = tmp
      
      tmp = simp_reform[i,"lr2"]
      simp_reform[i,"lr2"] = simp_reform[i,"lr1"]
      simp_reform[i,"lr1"] = tmp
      
      simp_reform[i,"dir"]=1
    }
  }
  
 reform_list[[n]] = simp_reform
}
saveRDS(reform_list, file = "results/cell_comm/v3/reform_list.RDS")
```

Plot interaction counts

```{r}
inter_counts_l = list()
for(n in names(net_names_l)){
  inter_counts = reshape2::dcast(data = net_names_l[[n]], 
                                 formula = SOURCE~TARGET, value.var = "count")
  rownames(inter_counts) = inter_counts[,1]
  inter_counts = inter_counts[,-1]
  
  pdf(paste0("results/cell_comm/v3/", n, "_number_of_interactions.pdf"), useDingbats = F, 
      height = 10, width = 10)
  pheatmap::pheatmap(inter_counts, clustering_method = "ward.D2", main = "All clusters")
  dev.off()
  inter_counts_l[[n]] = inter_counts
}
```

Making two cell type by interaction matrices: one has the ligand mean, another the receptor mean

```{r}
scoring_mats = list()
for(n in names(reform_list)){
  cl_reform = reform_list[[n]]
  # add reversed non-directed interactions, to consider them in both directions
  cl_reform0 = cl_reform[cl_reform$dir==0,]
  cl_reform0 = cl_reform0[,c(1,3,2,5,4,6,7)]
  colnames(cl_reform0) = colnames(cl_reform)
  cl_reform = rbind(cl_reform, cl_reform0)
  
  dec_cl = dec_l[[n]]
  
  mat_lig_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)), 
                   ncol = length(unique(cl_reform$ct1))))
  mat_rec_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)), 
                   ncol = length(unique(cl_reform$ct1))))
  colnames(mat_lig_cl) = colnames(mat_rec_cl) = unique(cl_reform$ct1)
  rownames(mat_lig_cl) = rownames(mat_rec_cl) = unique(cl_reform$id_cp_interaction)
  for(i in 1:nrow(cl_reform)){
    g1 = cl_reform[i,"lr1"]
    g2 = cl_reform[i,"lr2"]
    
    c1 = cl_reform[i,"ct1"]
    c2 = cl_reform[i,"ct2"]
    
    int = as.character(cl_reform[i,1])
    
    m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g1,c1], na.rm = T)
    if(is.na(m1)) m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g1,c1], 
                            na.rm = T)
    mat_lig_cl[int, c1] = m1
    
    m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g2,c2], na.rm = T)
    if(is.na(m2)) m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g2,c2],
                            na.rm = T)
    if(is.na(m1) | is.na(m2)) print(int)
    mat_rec_cl[int, c2] = m2
  }
  
  # ligand vs receptor correlation
  cor_mat_each_cl = cor(mat_lig_cl, mat_rec_cl, use="pairwise.complete.obs", method = "sp")
  
  scoring_mats[[n]] = list("mat_lig_ct" = mat_lig_cl, "mat_rec_ct" = mat_rec_cl,
                           "cor_mat_each" = cor_mat_each_cl)
  
  # sum(lig, reg) correlation
  mat_lig_cl[is.na(mat_lig_cl)] = 0
  mat_rec_cl[is.na(mat_rec_cl)] = 0
  mat_both_cl = mat_lig_cl+mat_rec_cl
  mat_both_cl[mat_both_cl==0] = NA
  cor_mat_both_cl = cor(mat_both_cl, use="pairwise.complete.obs", method = "sp")
  
  scoring_mats[[n]]$cor_mat_both = cor_mat_both_cl
}
saveRDS(scoring_mats, file = "results/cell_comm/v3/scoring_mats.RDS")
```

Making two cell type by interaction matrices: one has the ligand mean, another the receptor mean (here only directional)

```{r}
scoring_mats_dir = list()
for(n in names(reform_list)){
  cl_reform = reform_list[[n]]
  # add reversed non-directed interactions, to consider them in both directions
  cl_reform0 = cl_reform[cl_reform$dir==0,]
  cl_reform0 = cl_reform0[,c(1,3,2,5,4,6,7)]
  colnames(cl_reform0) = colnames(cl_reform)
  cl_reform = rbind(cl_reform, cl_reform0)
  
  cl_reform = cl_reform[cl_reform$dir==1,]
  
  dec_cl = dec_l[[n]]
  
  mat_lig_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)), 
                   ncol = length(unique(cl_reform$ct1))))
  mat_rec_cl = data.frame(matrix(NA, nrow = length(unique(cl_reform$id_cp_interaction)), 
                   ncol = length(unique(cl_reform$ct1))))
  colnames(mat_lig_cl) = colnames(mat_rec_cl) = unique(cl_reform$ct1)
  rownames(mat_lig_cl) = rownames(mat_rec_cl) = unique(cl_reform$id_cp_interaction)
  for(i in 1:nrow(cl_reform)){
    g1 = cl_reform[i,"lr1"]
    g2 = cl_reform[i,"lr2"]
    
    c1 = cl_reform[i,"ct1"]
    c2 = cl_reform[i,"ct2"]
    
    int = as.character(cl_reform[i,1])
    
    m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g1,c1], na.rm = T)
    if(is.na(m1)) m1 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g1,c1], 
                            na.rm = T)
    mat_lig_cl[int, c1] = m1
    
    m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,1]==g2,c2], na.rm = T)
    if(is.na(m2)) m2 = mean(dec_cl[dec_cl$id_cp_interaction==int & dec_cl[,5]==g2,c2],
                            na.rm = T)
    if(is.na(m1) | is.na(m2)) print(int)
    mat_rec_cl[int, c2] = m2
  }
  
  # ligand vs receptor correlation
  cor_mat_each_cl = cor(mat_lig_cl, mat_rec_cl, use="pairwise.complete.obs", method = "sp")
  
  scoring_mats_dir[[n]] = list("mat_lig_ct" = mat_lig_cl, "mat_rec_ct" = mat_rec_cl,
                               "cor_mat_each" = cor_mat_each_cl)
  
  # sum(lig, reg) correlation
  mat_lig_cl[is.na(mat_lig_cl)] = 0
  mat_rec_cl[is.na(mat_rec_cl)] = 0
  mat_both_cl = mat_lig_cl+mat_rec_cl
  mat_both_cl[mat_both_cl==0] = NA
  cor_mat_both_cl = cor(mat_both_cl, use="pairwise.complete.obs", method = "sp")
  
  scoring_mats_dir[[n]]$cor_mat_both = cor_mat_both_cl
}
saveRDS(scoring_mats_dir, file = "results/cell_comm/v3/scoring_mats_dir.RDS")
```

Count interactions of each type

```{r}
inter_counts_dir = list()
for(n in names(reform_list)){
  inter_counts_dir[[n]] = list()
  for(i in c(0,1)){
    cl_reform = reform_list[[n]]
    cl_reform = cl_reform[cl_reform$dir==i,]
    
    n_dir_int = matrix(0, nrow = length(unique(cl_reform$ct1)),
                       ncol = length(unique(cl_reform$ct1)))
    rownames(n_dir_int) = colnames(n_dir_int) = unique(cl_reform$ct1)
    for(c1 in unique(cl_reform$ct1)){
      for(c2 in unique(cl_reform$ct1)){
        n_dir_int[c1,c2] = nrow(cl_reform[cl_reform$ct1==c1 & cl_reform$ct2==c2,])
        if(i==0){
          n_dir_int[c1,c2] = n_dir_int[c1,c2]+nrow(cl_reform[cl_reform$ct1==c2 &
                                                               cl_reform$ct2==c1,])
        }
      }
    }
    inter_counts_dir[[n]][[as.character(i)]] = n_dir_int
  }
}
saveRDS(inter_counts_dir, file = "results/cell_comm/v3/inter_counts_dir.RDS")
```



# Interaction analysis in conditions
Compare interactions with DE genes

```{r}
# add genes from complexes
complex_ref = unique(rbind(dec_l$healthy[dec_l$healthy$is_complex=="True",c(1,5)], 
                           dec_l$embolised[dec_l$embolised$is_complex=="True",c(1,5)],
                           dec_l$regenerating[dec_l$regenerating$is_complex=="True",c(1,5)],
                           dec_l$healthy_simp[dec_l$healthy_simp$is_complex=="True",c(1,5)],
                           dec_l$embolised_simp[dec_l$embolised_simp$is_complex=="True",c(1,5)],
                           dec_l$regenerating_simp[dec_l$regenerating_simp$is_complex=="True",c(1,5)]))
inter_df = list()
for(n in names(reform_list)){
  tmp = merge(reform_list[[n]], complex_ref, by.x = 4, by.y = 2, all.x = T)[,c(2,3,4,1,5,8,6,7)]
  inter_df[[n]] = merge(tmp, complex_ref, by.x = 5, by.y = 2, all.x = T)[,c(2,3,4,5,6,1,9,7,8)]
  inter_df[[n]]$gene_name.x[is.na(inter_df[[n]]$gene_name.x)] = inter_df[[n]]$lr1[is.na(inter_df[[n]]$gene_name.x)]
  inter_df[[n]]$gene_name.y[is.na(inter_df[[n]]$gene_name.y)] = inter_df[[n]]$lr2[is.na(inter_df[[n]]$gene_name.y)]
  colnames(inter_df[[n]])[c(5,7)] = c("gn1", "gn2")
}

for(cc in names(inter_df)){
  # interaction unique in condition
  unique_inter = setdiff(unique(inter_df[[cc]]$id_cp_interaction),
                         unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$id_cp_interaction,
                                  inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$id_cp_interaction)))
  inter_df[[cc]]$cpdb_unique = inter_df[[cc]]$id_cp_interaction %in% unique_inter
  
  # lr1 unique in condition
  unique_lr1 = setdiff(unique(inter_df[[cc]]$lr1),
                         unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$lr1,
                                  inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$lr1)))
  inter_df[[cc]]$cpdb_unique_lr1 = inter_df[[cc]]$lr1 %in% unique_lr1
  
  # lr2 unique in condition
  unique_lr2 = setdiff(unique(inter_df[[cc]]$lr2),
                         unique(c(inter_df[[names(inter_df)[names(inter_df)!=cc][1]]]$lr2,
                                  inter_df[[names(inter_df)[names(inter_df)!=cc][2]]]$lr2)))
  inter_df[[cc]]$cpdb_unique_lr2 = inter_df[[cc]]$lr2 %in% unique_lr2
}
for(cc in names(inter_df)){
  oc1 = names(inter_df)[names(inter_df)!=cc][1]
  oc2 = names(inter_df)[names(inter_df)!=cc][2]
  int1 = paste0(inter_df[[cc]]$id_cp_interaction,inter_df[[cc]]$ct1,inter_df[[cc]]$ct2)
  int2 = unique(paste0(inter_df[[oc1]]$id_cp_interaction,inter_df[[oc1]]$ct1,inter_df[[oc1]]$ct2))
  int3 = unique(paste0(inter_df[[oc2]]$id_cp_interaction,inter_df[[oc2]]$ct1,inter_df[[oc2]]$ct2))
  unique_inter = setdiff(unique(int1), unique(c(int2, int3)))
  inter_df[[cc]]$cpdb_unique_ct = int1 %in% unique_inter
}
for(n in names(inter_df)){
  df = inter_df[[n]]
  df = unique(df[,c(1:3)])
  
  ints_un_ct = rowSums(table(df$id_cp_interaction, df$ct1)>0)<4 | 
    rowSums(table(df$id_cp_interaction, df$ct2)>0)<4
  
  inter_df[[n]]$inter_ct_spec = inter_df[[n]]$id_cp_interaction %in% names(ints_un_ct)[ints_un_ct]
}
for(n in names(inter_df)){
  xxx = data.frame(ct = c(inter_df[[n]][,2], inter_df[[n]][,3]), 
                   lr = c(inter_df[[n]][,4], inter_df[[n]][,6]))
  xxx = unique(xxx)
  tab_lr = (table(xxx$lr, xxx$ct)>0)*1
  tab_lr = rowSums(tab_lr)
  
  inter_df[[n]]$lr1_nct = tab_lr[inter_df[[n]]$lr1]
  inter_df[[n]]$lr2_nct = tab_lr[inter_df[[n]]$lr2]
}
saveRDS(inter_df, file = "results/cell_comm/v3/cond_diff_interact_DE.RDS")
```

Plot interaction heatmap - total and filtered

```{r, fig.height=10, fig.width=10}
int_counts_total = list()
int_counts_filt = list()
for(n in names(inter_df)){
  subdfct = unique(inter_df[[n]][,1:3])
  subdfct = unique(subdfct)
  dfct = data.frame("ct1" = c(subdfct$ct1, subdfct$ct2),
                    "ct2" = c(subdfct$ct2, subdfct$ct1))
  int_counts_total[[n]] = dfct
  pheatmap::pheatmap(table(dfct$ct1, dfct$ct2), main = n)
  
  keep = inter_df[[n]]$lr1_nct<=1 | inter_df[[n]]$lr2_nct<=1
  subdfct = unique(inter_df[[n]][keep,c(1:3, 15,16)])
  swp = subdfct[,5]==1
  tmp = subdfct$ct2[swp]
  subdfct$ct2[swp] = subdfct$ct1[swp]
  subdfct$ct1[swp] = tmp
  tmp = subdfct$lr2_nct[swp]
  subdfct$lr2_nct[swp] = subdfct$lr1_nct[swp]
  subdfct$lr1_nct[swp] = tmp
  dup = subdfct[subdfct$lr1_nct==1 & subdfct$lr2_nct==1,]
  tmp = dup$ct2
  dup$ct2 = dup$ct1
  dup$ct1 = tmp
  subdfct = rbind(subdfct, dup)
  subdfct = unique(subdfct[,1:3])
  int_counts_filt[[n]] = dfct
  pheatmap::pheatmap(table(subdfct$ct1, subdfct$ct2), main = n)
}
saveRDS(int_counts_total, file = "results/cell_comm/v3/int_counts_total.RDS")
saveRDS(int_counts_filt, file = "results/cell_comm/v3/int_counts_filt.RDS")
```

Get non-ubiquitous interactions

```{r}
inter_nonss = setdiff(unique(c(inter_df$embolised$id_cp_interaction,
                               inter_df$regenerating$id_cp_interaction)),
                      unique(inter_df$healthy$id_cp_interaction))
inter_nonemb = setdiff(unique(c(inter_df$healthy$id_cp_interaction,
                               inter_df$regenerating$id_cp_interaction)),
                      unique(inter_df$embolised$id_cp_interaction))
inter_nonreg = setdiff(unique(c(inter_df$embolised$id_cp_interaction,
                               inter_df$healthy$id_cp_interaction)),
                      unique(inter_df$regenerating$id_cp_interaction))

ct_g_cond = list()
for(n in names(inter_df)[1:3]){
  df = inter_df[[n]][inter_df[[n]]$cpdb_unique | 
                       inter_df[[n]]$id_cp_interaction %in% c(inter_nonss, inter_nonemb, inter_nonreg),]
  #df = df[df$lr1_nct<=1 | df$lr2_nct<=1,]
  
  cts = unique(c(df$ct1, df$ct2))
  ct_g_list = list()
  for(ct in cts){
    ct_g_list[[ct]] = data.frame("interact" = c(as.character(df$id_cp_interaction[df$ct1==ct]),
                                                as.character(df$id_cp_interaction[df$ct2==ct])),
                                 "gene" = c(as.character(df$gn1[df$ct1==ct]),
                                            as.character(df$gn2[df$ct2==ct])),
                                 "gene_target" = c(as.character(df$gn2[df$ct1==ct]),
                                                   as.character(df$gn1[df$ct2==ct])),
                                 "ct" = ct,
                                 "ct_target" = c(as.character(df$ct2[df$ct1==ct]),
                                                as.character(df$ct1[df$ct2==ct])),
                                 "cond" = n, stringsAsFactors = F)
  }
  ct_g_cond[[n]] = unique(Reduce(rbind, ct_g_list))
}
ct_g_cond = Reduce(rbind, ct_g_cond)
dim(ct_g_cond)
length(unique(ct_g_cond$interact))


inter_nonss = setdiff(unique(c(inter_df$embolised_simp$id_cp_interaction,
                               inter_df$regenerating_simp$id_cp_interaction)),
                      unique(inter_df$healthy_simp$id_cp_interaction))
inter_nonemb = setdiff(unique(c(inter_df$healthy_simp$id_cp_interaction,
                               inter_df$regenerating_simp$id_cp_interaction)),
                      unique(inter_df$embolised_simp$id_cp_interaction))
inter_nonreg = setdiff(unique(c(inter_df$embolised_simp$id_cp_interaction,
                               inter_df$healthy_simp$id_cp_interaction)),
                      unique(inter_df$regenerating_simp$id_cp_interaction))

cts_g_cond = list()
for(n in names(inter_df)[4:6]){
  df = inter_df[[n]][inter_df[[n]]$cpdb_unique | 
                       inter_df[[n]]$id_cp_interaction %in% c(inter_nonss, inter_nonemb, inter_nonreg),]
  #df = df[df$lr1_nct<=1 | df$lr2_nct<=1,]
  
  cts = unique(c(df$ct1, df$ct2))
  ct_g_list = list()
  for(ct in cts){
    ct_g_list[[ct]] = data.frame("interact" = c(as.character(df$id_cp_interaction[df$ct1==ct]),
                                                as.character(df$id_cp_interaction[df$ct2==ct])),
                                 "gene" = c(as.character(df$gn1[df$ct1==ct]),
                                            as.character(df$gn2[df$ct2==ct])),
                                 "gene_target" = c(as.character(df$gn2[df$ct1==ct]),
                                                   as.character(df$gn1[df$ct2==ct])),
                                 "ct" = ct,
                                 "ct_target" = c(as.character(df$ct2[df$ct1==ct]),
                                                as.character(df$ct1[df$ct2==ct])),
                                 "cond" = n, stringsAsFactors = F)
  }
  cts_g_cond[[n]] = unique(Reduce(rbind, ct_g_list))
}
cts_g_cond = Reduce(rbind, cts_g_cond)
dim(cts_g_cond)
length(unique(cts_g_cond$interact))

ct_g_l = list("cts_g_cond" = cts_g_cond,
              "ct_g_cond" = ct_g_cond)
```

Annotate interactions

```{r}
#xxx = merge(unique(rbind(ct_g_cond[,1:3], cts_g_cond[,1:3])), 
#            unique(inter_annot[,c(1,4)]), by = 1, all.x = T)
#write.csv(xxx, file = "results/cell_comm/v3/inter_unique5.csv", row.names = F, col.names = T, quote = F)
inter_annot = read.csv("results/cell_comm/updt/inter_unique5.csv", header = T, stringsAsFactors = F)

inter_annot = unique(inter_annot[,c(1,4)])

description = strsplit(inter_annot$description, ";")
inter_des = lapply(1:length(description), 
                   function(x) rep(inter_annot$interact[x], length(description[[x]])))
inter_annot = data.frame("inter" = unlist(inter_des),
                         "funct" = unlist(description))

inter_annot$funct[inter_annot$funct=="intercellular adhesion"] = "adhesion"
inter_annot$funct[inter_annot$funct=="antibody regulation"] = "immune regulation"
inter_annot$funct[inter_annot$funct=="antigen presentation"] = "immune activity"

write.csv(inter_annot, file = "data/interaction_annotation2.csv", 
          row.names = F, col.names = T, quote = F)
colnames(inter_annot) = c("interact", "description")

ct_g_cond_ann_l = list()
for(n in names(ct_g_l)){
  ct_g_cond_ann = merge(ct_g_l[[n]], inter_annot, by = 1, all.x = T)
  ct_g_cond_ann = unique(ct_g_cond_ann[,c(1,4,5,6,7)])
  
  ct_g_cond_ann = data.frame("interactions" = rep(as.character(ct_g_cond_ann$interact),2),
                             "celltypes" = c(as.character(ct_g_cond_ann$ct),
                                             as.character(ct_g_cond_ann$ct_target)),
                             "condition" = rep(as.character(ct_g_cond_ann$cond),2),
                             "description" = rep(as.character(ct_g_cond_ann$description),2))
  ct_g_cond_ann$condition = factor(unlist(lapply(strsplit(ct_g_cond_ann$condition, "_"), 
                                                 function(x) x[1])), 
                                   levels = c("healthy", "embolised", "regenerating"))
  
  plt_bar_func = ggplot(ct_g_cond_ann, aes(x = condition, fill = condition))+
    facet_grid(description~celltypes, scales = "free_y")+
    geom_bar()+
    theme_bw()+
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
          legend.position = "none")
  
  pdf(paste0("results/cell_comm/v3/", n, "_plt_bar_func.pdf"), height = 20, width = 50)
  print(plt_bar_func)
  dev.off()
  
  ct_g_cond_ann_l[[n]] = ct_g_cond_ann
  saveRDS(ct_g_cond_ann, file = paste0("results/cell_comm/v3/", n, "_ann.RDS"))
}
```

Get expression for each interaction in each condition

```{r}
exp_list = list()
int_groups = list("cts_g_cond" = unique(c(sig_means_names_l$healthy_simp$id_cp_interaction,
                                    sig_means_names_l$embolised_simp$id_cp_interaction,
                                    sig_means_names_l$regenerating_simp$id_cp_interaction)),
                  "ct_g_cond" = unique(c(sig_means_names_l$healthy$id_cp_interaction,
                                    sig_means_names_l$embolised$id_cp_interaction,
                                    sig_means_names_l$regenerating$id_cp_interaction)))
for(n in names(sig_means_names_l)){
  df = sig_means_names_l[[n]][,c(1,5:6,13:ncol(sig_means_names_l[[n]]))]
  
  #colnames(df) = gsub(".", " ", colnames(df), fixed = T)
  colnames(df) = gsub("gd.T.cells", "gd-T cells", colnames(df), fixed = T)
  colnames(df) = gsub("B.cells", "B cells", colnames(df), fixed = T)
  colnames(df) = gsub("Dividing.endothelial.cells", "Dividing endothelial cells", 
                      colnames(df), fixed = T)
  colnames(df) = gsub("Dividing.cDCs", "Dividing cDCs", colnames(df), fixed = T)
  colnames(df) = gsub("Infiltrating.NK.cells", "Infiltrating NK cells", colnames(df), fixed = T)
  colnames(df) = gsub("Macrophages..HES4..", "Macrophages (HES4+)", colnames(df), fixed = T)
  colnames(df) = gsub("activated.DCs", "activated DCs", colnames(df), fixed = T)
  colnames(df) = gsub("Pericentral.LSEC", "Pericentral LSEC", colnames(df), fixed = T)
  colnames(df) = gsub("Midzonal.LSEC", "Midzonal LSEC", colnames(df), fixed = T)
  colnames(df) = gsub("Periportal.LSEC", "Periportal LSEC", colnames(df), fixed = T)
  colnames(df) = gsub("Periportal.LSEC", "Periportal LSEC", colnames(df), fixed = T)
  colnames(df) = gsub("NK.cells.1", "NK cells 1", colnames(df), fixed = T)
  colnames(df) = gsub("NK.cells.2", "NK cells 2", colnames(df), fixed = T)
  colnames(df) = gsub("NK.cells.3", "NK cells 3", colnames(df), fixed = T)
  colnames(df) = gsub("NK.cells", "NK cells", colnames(df), fixed = T)
  colnames(df) = gsub("CD8.ab.T.cells.1", "CD8 ab-T cells 1", colnames(df), fixed = T)
  colnames(df) = gsub("CD8.ab.T.cells.2", "CD8 ab-T cells 2", colnames(df), fixed = T)
  colnames(df) = gsub("CD8.ab.T.cells.3", "CD8 ab-T cells 3", colnames(df), fixed = T)
  colnames(df) = gsub("CD8.ab.T.cells", "CD8 ab-T cells", colnames(df), fixed = T)
  colnames(df) = gsub("Naive.CD4..T.cells", "Naive CD4+ T cells", colnames(df), fixed = T)
  colnames(df) = gsub("ab.T.cells..stress.", "ab-T cells (stress)", colnames(df), fixed = T)
  colnames(df) = gsub("ab.T.cells..stress.", "ab-T cells (stress)", colnames(df), fixed = T)
  colnames(df) = gsub("Fibroblasts.1", "Fibroblasts 1", colnames(df), fixed = T)
  colnames(df) = gsub("Fibroblasts.2", "Fibroblasts 2", colnames(df), fixed = T)
  colnames(df) = gsub("Fibroblasts.3", "Fibroblasts 3", colnames(df), fixed = T)
  colnames(df) = gsub("Monocytes..secretory.", "Monocytes (secretory)", colnames(df), fixed = T)
  colnames(df) = gsub("Monocytes..TREM2..CD9..", "Monocytes (TREM2+ CD9+)", colnames(df), fixed = T)
  colnames(df) = gsub("Monocytes..IGSF21..GPR34..", "Monocytes (IGSF21+ GPR34+)", 
                      colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..stress.", "LSEC (stress)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..remodelling.", "LSEC (remodelling)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..interferon.", "LSEC (interferon)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..high.MT.2.", "LSEC (high MT 2)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..high.MT.1.", "LSEC (high MT 1)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..high.MT.", "LSEC (high MT)", colnames(df), fixed = T)
  colnames(df) = gsub("LSEC..fenestr..", "LSEC (fenestr.)", colnames(df), fixed = T)
  colnames(df) = gsub("Kupffer.cells..SUCNR1..", "Kupffer cells (SUCNR1+)", colnames(df), fixed = T)
  colnames(df) = gsub("IgG..Plasma.cells", "IgG+ Plasma cells", colnames(df), fixed = T)
  colnames(df) = gsub("IgA..Plasma.cells", "IgA+ Plasma cells", colnames(df), fixed = T)
  colnames(df) = gsub("EC.non.LSEC", "EC non-LSEC", colnames(df), fixed = T)
  colnames(df) = gsub("Dividing.T.NK.cells", "Dividing T/NK cells", colnames(df), fixed = T)
  colnames(df) = gsub("Kupffer.cells", "Kupffer cells", colnames(df), fixed = T)
  colnames(df) = gsub("TRM.cells", "TRM cells", colnames(df), fixed = T)
  colnames(df) = gsub("MAIT.cells.1", "MAIT cells 1", colnames(df), fixed = T)
  colnames(df) = gsub("MAIT.cells.2", "MAIT cells 2", colnames(df), fixed = T)
  colnames(df) = gsub("MAIT.cells", "MAIT cells", colnames(df), fixed = T)
  colnames(df) = gsub("Lymphatic.EC", "Lymphatic EC", colnames(df), fixed = T)
  colnames(df) = gsub("Stellate.cells", "Stellate cells", colnames(df), fixed = T)
  # head(colnames(df), 60)
  
  df = reshape2::melt(df, id.vars = 1:3)
  df$value[is.na(df$value)] = 0
  df = df %>%
   group_by(id_cp_interaction, gene_a, gene_b, variable) %>%
   summarise(value=(mean(value)), .groups = "keep")
  df = as.data.frame(df)
  
  ns = if(grepl("simp", n)) "cts_g_cond" else "ct_g_cond"
  ct_g_cond_ann = ct_g_cond_ann_l[[ns]]
  sub_df = df[df$id_cp_interaction %in% ct_g_cond_ann$interactions,]
  sub_df = merge(ct_g_l[[ns]], sub_df, all = T, by = 1)
  sub_df = sub_df[sub_df$interact %in% int_groups[[ns]],]
  sub_df$variable = as.character(sub_df$variable)
  sub_df$value[is.na(sub_df$value)] = 0
  
  ct1 = unlist(lapply(strsplit(sub_df$variable, ".", fixed = T), function(x) x[1]))
  ct2 = unlist(lapply(strsplit(sub_df$variable, ".", fixed = T), function(x) x[2]))
  keep = sapply(1:nrow(sub_df), function(x) (sub_df$ct[x]==ct1[x] & sub_df$ct_target[x]==ct2[x]) |
                  (sub_df$ct[x]==ct2[x] & sub_df$ct_target[x]==ct1[x]))
  exp_list[[n]] = sub_df[keep,c(1:6,10)]
}
for(n in names(exp_list)){
  exp_list[[n]] = exp_list[[n]][!is.na(exp_list[[n]]$interact),]
}

ct_int_exp_l = list()
for(simp in c(T, F)){
  n_use = names(exp_list)[grepl("simp", names(exp_list))==simp]
  #ct_int_exp = cbind(exp_list[[n_use[1]]], exp_list[[n_use[3]]]$value, exp_list[[n_use[3]]]$value)
  ct_int_exp = merge(exp_list[[n_use[1]]], exp_list[[n_use[2]]], by = 1:6)
  ct_int_exp = merge(ct_int_exp, exp_list[[n_use[3]]], by = 1:6)
  ct_int_exp$value.x[is.na(ct_int_exp$value.x)] = ct_int_exp$value.y[is.na(ct_int_exp$value.y)] = ct_int_exp$value[is.na(ct_int_exp$value)] = 0
  colnames(ct_int_exp)[7:9] = c("healthy_exp", "embolised_exp", "regenerating_exp")
  ct_int_exp = ct_int_exp[ct_int_exp$healthy_exp>0 | 
                            ct_int_exp$embolised_exp>0 | 
                            ct_int_exp$regenerating_exp>0,]

  tup_list = list()
  keep_row = c()
  for(i in 1:nrow(ct_int_exp)){
    tup1 = paste(c(ct_int_exp$gene[i], ct_int_exp$gene_target[i], ct_int_exp$ct[i], 
                   ct_int_exp$ct_target[i], ct_int_exp$cond[i]), collapse = " ")
    tup2 = paste(c(ct_int_exp$gene_target[i], ct_int_exp$gene[i], ct_int_exp$ct_target[i], 
                   ct_int_exp$ct[i], ct_int_exp$cond[i]), collapse = " ")
    if(!(tup1 %in% tup_list) & !(tup2 %in% tup_list)){
      tup_list = c(tup_list, tup1, tup2)
      keep_row = c(keep_row, T)
    } else{
      keep_row = c(keep_row, F)
    }
  }
  ct_int_exp = ct_int_exp[keep_row,]
  
  ct_int_exp = merge(ct_int_exp, unique(ct_g_cond_ann[,c(1,4)]), by = 1, all = T)
  nn = if(simp) "simp" else "all"
  ct_int_exp_l[[nn]] = ct_int_exp
  ct_int_exp_l[[nn]] = ct_int_exp_l[[nn]][complete.cases(ct_int_exp_l[[nn]]),]
  
  ct_int_exp_l[[nn]]$cond = unlist(lapply(strsplit(ct_int_exp_l[[nn]]$cond, "_"), function(x) x[1]))
}

saveRDS(ct_int_exp_l, file = "results/cell_comm/v3/interact_celltype_exp_group_list.RDS")
```

Variability of interactions

```{r}
inter_annot = read.csv("data/interaction_annotation2.csv", header = T)
inter_annot$funct[grepl("imm", inter_annot$funct)] = "immune"
inter_annot$funct[grepl("inflam", inter_annot$funct)] = "immune"
gr = inter_annot$funct
names(gr) = inter_annot$inter
gr_list = tapply(inter_annot$inter, inter_annot$funct, function(x) x)

her_allint_l = list()
for(simp in c(T, F)){
  n_use = names(reform_list)[grepl("simp", names(reform_list))==simp]
  
  he_allint = merge(reform_list[[n_use[1]]][,1:6], reform_list[[n_use[2]]][,1:6], by = 1:3, all = T)
  he_allint$lr1.x[is.na(he_allint$lr1.x)] = he_allint$lr1.y[is.na(he_allint$lr1.x)]
  he_allint$lr2.x[is.na(he_allint$lr2.x)] = he_allint$lr2.y[is.na(he_allint$lr2.x)]
  he_allint = he_allint[,c(1:6,9)]
  her_allint = merge(he_allint, reform_list[[n_use[3]]][,1:6], by = 1:3, all = T)
  her_allint$lr1.x[is.na(her_allint$lr1.x)] = her_allint$lr1[is.na(her_allint$lr1.x)]
  her_allint$lr2.x[is.na(her_allint$lr2.x)] = her_allint$lr2[is.na(her_allint$lr2.x)]
  her_allint = her_allint[,c(1:7,10)]
  her_allint[is.na(her_allint)] = 0
  colnames(her_allint)[4:8] = c("lr1", "lr2", "healthy_exp", "embolised_exp", "regenerating_exp")
  
  # count occurrences per condition. this works bc we're already working with sig means
  her_allint = merge(her_allint, data.frame(table(her_allint$id_cp_interaction[her_allint$healthy_exp>0])), 
                                            by = 1, all.x = T)
  her_allint = merge(her_allint, data.frame(table(her_allint$id_cp_interaction[her_allint$embolised_exp>0])), 
                                            by = 1, all.x = T)
  her_allint = merge(her_allint, 
                     data.frame(table(her_allint$id_cp_interaction[her_allint$regenerating_exp>0])), 
                     by = 1, all.x = T)
  colnames(her_allint)[9:11] = c("healthy_n", "embolised_n", "regenerating_n")
  her_allint[is.na(her_allint)] = 0
  her_allint$ct_pair = factor(paste0(her_allint$ct1, "_", her_allint$ct2))
  
  comb_cond = combn(colnames(her_allint)[6:8],2)
  colnames(comb_cond) = c("he", "hr", "er")
  for(i in colnames(comb_cond)){
    plot_df = her_allint[her_allint[,comb_cond[1,i]]>0 | her_allint[,comb_cond[2,i]]>0,1:12]
    
    exp_df1 = reshape2::dcast(plot_df, formula = id_cp_interaction ~ ct_pair, 
                              value.var = comb_cond[1,i], fill = 0)
    rownames(exp_df1) = exp_df1[,1]
    exp_df1 = exp_df1[,-1]>0
    exp_df2 = reshape2::dcast(plot_df, formula = id_cp_interaction ~ ct_pair, 
                              value.var = comb_cond[2,i], fill = 0)
    rownames(exp_df2) = exp_df2[,1]
    exp_df2 = exp_df2[,-1]>0
    
    plot_df = merge(plot_df, sapply(rownames(exp_df1), 
                                    function(x) infotheo::mutinformation(exp_df1[x,], exp_df2[x,])),
                    by.x = 1, by.y = 0, all.x = T)
    plot_df = merge(plot_df, sapply(rownames(exp_df1), 
                                    function(x) e1071::hamming.distance(exp_df1[x,], exp_df2[x,])),
                    by.x = 1, by.y = 0, all.x = T)
    plot_df = merge(plot_df, sapply(rownames(exp_df1), 
                                    function(x) e1071::hamming.distance(exp_df1[x,],
                                                                        exp_df2[x,])/sum(exp_df1[x,] |
                                                                                           exp_df2[x,])),
                    by.x = 1, by.y = 0, all.x = T)
    plot_df = merge(plot_df, sapply(rownames(exp_df1), 
                                    function(x) sum(exp_df1[x,] | exp_df2[x,])),
                    by.x = 1, by.y = 0, all.x = T)
    
    colnames(plot_df)[13:16] = paste0(c("mutInfo_", "hamm_", "hammNorm_", "tot_"), i)
    
    her_allint = merge(her_allint, unique(plot_df[,c(1,13:16)]), all.x = T, by = 1)
  }
  her_allint$mutInfo_er[is.na(her_allint$mutInfo_er)] = 1
  her_allint$mutInfo_hr[is.na(her_allint$mutInfo_hr)] = 1
  her_allint$mutInfo_he[is.na(her_allint$mutInfo_he)] = 1
  her_allint$tot_he[is.na(her_allint$tot_he)] = 0
  her_allint$tot_hr[is.na(her_allint$tot_hr)] = 0
  her_allint$tot_er[is.na(her_allint$tot_er)] = 0
  her_allint$hamm_er[is.na(her_allint$hamm_er)] = 0
  her_allint$hamm_hr[is.na(her_allint$hamm_hr)] = 0
  her_allint$hamm_he[is.na(her_allint$hamm_he)] = 0
  her_allint$hammNorm_er[is.na(her_allint$hammNorm_er)] = 0
  her_allint$hammNorm_he[is.na(her_allint$hammNorm_he)] = 0
  her_allint$hammNorm_hr[is.na(her_allint$hammNorm_hr)] = 0
  
  her_allint$diff_n_he = her_allint$embolised_n-her_allint$healthy_n
  her_allint$diff_n_hr = her_allint$regenerating_n-her_allint$healthy_n
  her_allint$diff_n_er = her_allint$regenerating_n-her_allint$embolised_n
  
  # plot tot vs mutual
  plot_df = unique(her_allint[,c("id_cp_interaction", paste0(c("mutInfo_", "tot_", "diff_n_"), i))])
  plt = ggplot(plot_df, aes(x = tot_er, y = mutInfo_er*(diff_n_er/abs(diff_n_er))))+
    geom_bin2d()+
    scale_x_log10()+
    theme_bw()+
    theme(aspect.ratio = 1)
  print(plt)
  
  nn = if(simp) "simp" else "all"
  her_allint_l[[nn]] = her_allint
}

saveRDS(her_allint_l, file = "./results/cell_comm/v3/interactions_mutInfo_condComp.RDS")
```

GSEA of interactions using mutual information

```{r}
for(n in names(her_allint_l)){
  her_allint = her_allint_l[[n]]
  
  comb_cond = combn(colnames(her_allint)[6:8],2)
  colnames(comb_cond) = c("he", "hr", "er")
  gsea_list = list()
  for(i in colnames(comb_cond)){
    vals = unique(her_allint[,c("id_cp_interaction", paste0("mutInfo_", i))])
    vals2 = vals[,paste0("mutInfo_", i)] 
    names(vals2) = vals$id_cp_interaction
    
    set.seed(1)
    gsea_list[[i]] = liger::bulk.gsea(1-vals2, set.list = gr_list, n.rand = 1000000)
    gsea_list[[i]]$group = rownames(gsea_list[[i]])
    gsea_list[[i]]$comp = i
  }
  
  liger::gsea(1-vals2, geneset = gr_list$immune, plot = T)
  
  # plot GSEA enrichment
  plot_df = Reduce(rbind, gsea_list)
  plot_df$q.val = -log10(plot_df$q.val+0.0000005)*(plot_df$sscore/abs(plot_df$sscore))
  plot_df$comp = factor(plot_df$comp, levels = rev(c("he","hr","er")))
  m = tapply(plot_df$q.val, plot_df$group,mean)
  plot_df$group = factor(plot_df$group, levels = names(m)[order(m, decreasing = F)])
  plt = ggplot(plot_df, aes(x = q.val, y = group, fill = comp))+
    geom_col(position = "dodge")+
    geom_vline(xintercept = c(log10(0.05), -log10(0.05)), linetype = "dashed")+
    geom_vline(xintercept = c(0))+
    labs(x = "q-value x score signal", y = "interaction type", fill = "comparison")+
    theme_bw()+
    theme(axis.text = element_text(colour = "black", size = 7),
          axis.title = element_text(colour = "black", size = 7.5),
          legend.position = c(0,1),
          legend.justification = c(-0.05,1.05),
          legend.title = element_text(size = 7.5),
          legend.text = element_text(size = 7),
          legend.margin = margin(0,0,0,0),
          legend.key.size = unit(0.35, "cm"))
  print(plt)
  
  saveRDS(gsea_list, file = paste0("./results/cell_comm/v3/", n,
                                   "gsea_interactions_mutInfo_condComp.RDS"))
}
```

Save mutual information for LR and cell types

```{r}
for(n in names(her_allint_l)){
  her_allint = her_allint_l[[n]]
  # select genes from lowest mutual (table - get most common)
  # +
  # mean mutual per cell type (lowest = more change)
  ## plot interactions based on those genes (some are involved in more than one)
  ## heatmap - rows ct; columns genes; gaps between interact; 1 heatmap/cond, same ct ordering
  sub_df1 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","ct1","mutInfo_he")])
  sub_df2 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","ct2","mutInfo_he")])
  ct_mut_he = tapply(c(sub_df1$mutInfo_he, sub_df2$mutInfo_he), 
                     c(sub_df1$ct1, sub_df2$ct2), mean)
  sub_df1 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","ct1","mutInfo_hr")])
  sub_df2 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","ct2","mutInfo_hr")])
  ct_mut_hr = tapply(c(sub_df1$mutInfo_hr, sub_df2$mutInfo_hr), 
                     c(sub_df1$ct1, sub_df2$ct2), mean)
  sub_df1 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","ct1","mutInfo_er")])
  sub_df2 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","ct2","mutInfo_er")])
  ct_mut_er = tapply(c(sub_df1$mutInfo_er, sub_df2$mutInfo_er), 
                     c(sub_df1$ct1, sub_df2$ct2), mean)
  ct_mut_df = cbind(ct_mut_he,ct_mut_hr,ct_mut_er)
  rownames(ct_mut_df) = names(ct_mut_he)
  colnames(ct_mut_df) = c("he", "hr", "er")
  saveRDS(ct_mut_df, file = "./results/cell_comm/v3/ct_select_mutInfo_condComp.RDS")
  
  sub_df1 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","lr1","mutInfo_he")])
  sub_df2 = unique(her_allint[her_allint$tot_he>0,c("id_cp_interaction","lr2","mutInfo_he")])
  lr_mut_he = tapply(c(sub_df1$mutInfo_he, sub_df2$mutInfo_he), 
                     c(sub_df1$lr1, sub_df2$lr2), mean)
  sub_df1 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","lr1","mutInfo_hr")])
  sub_df2 = unique(her_allint[her_allint$tot_hr>0,c("id_cp_interaction","lr2","mutInfo_hr")])
  lr_mut_hr = tapply(c(sub_df1$mutInfo_hr, sub_df2$mutInfo_hr), 
                     c(sub_df1$lr1, sub_df2$lr2), mean)
  sub_df1 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","lr1","mutInfo_er")])
  sub_df2 = unique(her_allint[her_allint$tot_er>0,c("id_cp_interaction","lr2","mutInfo_er")])
  lr_mut_er = tapply(c(sub_df1$mutInfo_er, sub_df2$mutInfo_er), 
                     c(sub_df1$lr1, sub_df2$lr2), mean)
  
  lr_cnt_he = table(c(her_allint[her_allint$tot_he>0 & her_allint$mutInfo_he<=0.05,"lr1"],
                      her_allint[her_allint$tot_he>0 & her_allint$mutInfo_he<=0.05,"lr2"]))
  lr_he = merge(lr_cnt_he, lr_mut_he, by.x = 1, by.y = 0)
  lr_cnt_hr = table(c(her_allint[her_allint$tot_hr>0 & her_allint$mutInfo_hr<=0.05,"lr1"],
                      her_allint[her_allint$tot_hr>0 & her_allint$mutInfo_hr<=0.05,"lr2"]))
  lr_hr = merge(lr_cnt_hr, lr_mut_hr, by.x = 1, by.y = 0)
  lr_cnt_er = table(c(her_allint[her_allint$tot_er>0 & her_allint$mutInfo_er<=0.05,"lr1"],
                      her_allint[her_allint$tot_er>0 & her_allint$mutInfo_er<=0.05,"lr2"]))
  lr_er = merge(lr_cnt_er, lr_mut_er, by.x = 1, by.y = 0)
  
  # ECM - which proteins/collagens?; mention TGFB
  # dev - which ligands/receptors
  lr_all = rbind(lr_he, lr_hr, lr_er)
  lr_all$cond = c(rep("he", nrow(lr_he)), rep("hr", nrow(lr_hr)),rep("er", nrow(lr_er)))
  colnames(lr_all) = c("lr", "n_mut.05", "mean_mutInfo", "cond")
  saveRDS(lr_all, file = paste0("./results/cell_comm/v3/", n, "LR_select_mutInfo_condComp.RDS"))
}
```

Plot interactions

```{r}
for(n in names(ct_int_exp_l)){
  ct_int_exp = ct_int_exp_l[[n]]
  r = if(n=="all") 1:3 else 4:6
  
  # interactions
  intdf = unique(Reduce(rbind, reform_list[r])[,c(1,4,5)])
  intdf$intpair = paste0(intdf$lr1, " - ", intdf$lr2)
  
  ct_int_exp_file = unique(ct_int_exp[,c(1,4:5,7:10)])
  ct_int_exp_file$ctpair = paste0(ct_int_exp_file$ct, " - ", ct_int_exp_file$ct_target)
  
  plot_df_int = reshape2::melt(ct_int_exp_file[,c(1,8,4:7)])
  plot_df_int$variable = unlist(lapply(strsplit(as.character(plot_df_int$variable), "_"), 
                                       function(x) x[[1]][1]))
  plot_df_int$variable = factor(plot_df_int$variable, 
                                levels = rev(c("healthy", "embolised", "regenerating")))
  
  sub_plot_df_int = plot_df_int[grepl("Stellate", plot_df_int$ctpair) |
                                  grepl("Kupffer", plot_df_int$ctpair) |
                                  grepl("LSEC", plot_df_int$ctpair),]
  
  sub_plot_df_int = merge(sub_plot_df_int, intdf[,c(1,4)], by = 1, all.x = T)
  
  gg = "ECM"
  plt = ggplot(sub_plot_df_int[sub_plot_df_int$description==gg,], 
         aes(x = ctpair, y = variable, colour = value, size = value))+
    facet_grid(intpair~.)+
    guides(size = guide_legend(title = "exp", reverse = T), 
           colour = guide_legend(title = "exp", reverse = T))+
    geom_point()+
    labs(title = gg)+
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
          strip.text.y = element_text(angle = 0, size = 8))
  print(plt)
}
```

Important tables

```{r}
for(n in names(inter_df)){
  write.csv(inter_df[[n]], col.names = T, row.names = F, quote = F,
            file = paste0("results/cell_comm/v3/tables/Interact_", n, "_celltypes_cond.csv"))
}
for(n in names(ct_int_exp_l)){
  ct_int_exp = ct_int_exp_l[[n]]
  write.csv(ct_int_exp, col.names = T, row.names = F, quote = F, 
            file = paste0("results/cell_comm/v3/tables/", n, "interact_celltype_exp_group.csv"))
}
```


## Cell-cell communication networks
Load data to make cell comm networks (NOT USED HERE)

```{r}
redone_meta = list()
for(cc in unique(allcells_css$Condition)){
  redone_meta[[cc]] = read.table(paste0("results/cell_comm/CellPhoneDB_V3/", 
                                        cc, "/", cc, "_meta_names.txt"), 
                                 sep = "\t", header = T, row.names = 1)
}
redone_meta_all = Reduce(rbind, redone_meta)

allcells_redone = AddMetaData(allcells_css, redone_meta_all)
allcells_redone = allcells_redone[,!is.na(allcells_redone$cell_type)]
```

Functions used to make cell comm networks

```{r}
makeMedian = function(point_df, edge_df, cl = c("ct2", "maj_g1", "maj_g2")){
  mean_major = data.frame("X1" = tapply(point_df$X1, point_df[,cl[1]], median),
                          "X2" = tapply(point_df$X2, point_df[,cl[1]], median))
  mean_major[,cl[1]] = rownames(mean_major)
  
  edge_df[,cl[2]] = factor(edge_df[,cl[2]], levels = unique(c(edge_df[,cl[2]], edge_df[,cl[3]])))
  edge_df[,cl[3]] = factor(edge_df[,cl[3]], levels = unique(c(as.character(edge_df[,cl[2]]),
                                                              edge_df[,cl[3]])))
  
  maj_mat = table(edge_df[,cl[2]], edge_df[,cl[3]])
  diag(maj_mat) = 0
  edge_major = data.frame(maj_mat + t(maj_mat))
  edge_major = merge(edge_major, mean_major, by.x = 1, by.y = 3)
  edge_major = merge(edge_major, mean_major, by.x = 2, by.y = 3)
  
  clcomb = combn(unique(c(as.character(edge_major$Var1), as.character(edge_major$Var2))), 2)
  keep = c()
  for(j in 1:ncol(clcomb)){
    keep = c(keep, which(edge_major$Var2==clcomb[1,j] & edge_major$Var1==clcomb[2,j]))
  }
  edge_major = edge_major[keep,]
  
  return(list(mean_major = mean_major, edge_major = edge_major))
}

makeMedianCond = function(point_df, edge_df, cl = c("ct", "ct_g1", "ct_g2"), edge_by = "condition"){
  mean_major = data.frame("X1" = tapply(point_df$X1, point_df[,cl[1]], median),
                          "X2" = tapply(point_df$X2, point_df[,cl[1]], median))
  mean_major[,cl[1]] = rownames(mean_major)
  
  edge_df[,cl[2]] = factor(edge_df[,cl[2]], levels = unique(c(edge_df[,cl[2]], edge_df[,cl[3]])))
  edge_df[,cl[3]] = factor(edge_df[,cl[3]], levels = unique(c(as.character(edge_df[,cl[2]]),
                                                              edge_df[,cl[3]])))
  
  edge_l = list()
  for(i in unique(edge_df[,edge_by])){
    sub_edge_df = edge_df[edge_df[,edge_by]==i,]
    maj_mat = table(sub_edge_df[,cl[2]], sub_edge_df[,cl[3]])
    diag(maj_mat) = 0
    edge_major = data.frame(maj_mat + t(maj_mat))
    edge_major = merge(edge_major, mean_major, by.x = 1, by.y = 3)
    edge_major = merge(edge_major, mean_major, by.x = 2, by.y = 3)
    
    # remove repeated
    clcomb = combn(unique(c(as.character(edge_major$Var1), as.character(edge_major$Var2))), 2)
    keep = c()
    for(j in 1:ncol(clcomb)){
      keep = c(keep, which(edge_major$Var2==clcomb[1,j] & edge_major$Var1==clcomb[2,j]))
    }
    
    edge_l[[i]] = edge_major[keep,]
  }
  edge_major = Reduce(rbind, edge_l)
  edge_major[,edge_by] = unlist(lapply(names(edge_l), function(x) rep(x, nrow(edge_l[[x]]))))
  edge_major = edge_major[edge_major$Var2!=edge_major$Var1,]
  
  return(list(mean_major = mean_major, edge_major = edge_major))
}
```

Prepare data

```{r, fig.width=16, fig.height=4.6}
inter_df = readRDS(file = "results/cell_comm/v3/cond_diff_interact_DE.RDS")

# interactions unique to each condition
unique_inters = c(setdiff(inter_df$healthy$id_cp_interaction, 
                          c(inter_df$embolised$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
                  setdiff(inter_df$embolised$id_cp_interaction, 
                          c(inter_df$healthy$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
                  setdiff(inter_df$regenerating$id_cp_interaction, 
                          c(inter_df$embolised$id_cp_interaction, inter_df$healthy$id_cp_interaction)))

# interactions unique to healthy or to emb/regen
comph_inters = c(setdiff(inter_df$healthy$id_cp_interaction, 
                          c(inter_df$embolised$id_cp_interaction, inter_df$regenerating$id_cp_interaction)),
                 setdiff(inter_df$embolised$id_cp_interaction, inter_df$healthy$id_cp_interaction),
                 setdiff(inter_df$regenerating$id_cp_interaction, inter_df$healthy$id_cp_interaction))

# prepare gene pairs per condition
gene_pairs_cond = rbind(unique(inter_df$healthy[,c("id_cp_interaction", "gn1", "gn2")]),
                        unique(inter_df$embolised[,c("id_cp_interaction", "gn1", "gn2")]),
                        unique(inter_df$regenerating[,c("id_cp_interaction", "gn1", "gn2")]))
gene_pairs_cond$condition = c(rep("healthy", nrow(unique(inter_df$healthy[,c("id_cp_interaction", 
                                                                             "gn1", "gn2")]))),
                              rep("embolised", nrow(unique(inter_df$embolised[,c("id_cp_interaction", 
                                                                                 "gn1", "gn2")]))),
                              rep("regenerating", nrow(unique(inter_df$regenerating[,c("id_cp_interaction",
                                                                                       "gn1", "gn2")]))))

# list all LR genes
all_lr_genes = unique(c(as.character(inter_df$healthy$gn1), as.character(inter_df$healthy$gn2),
                        as.character(inter_df$embolised$gn1), as.character(inter_df$embolised$gn2),
                        as.character(inter_df$regenerating$gn1), as.character(inter_df$regenerating$gn2)))
all_lr_genes = all_lr_genes[all_lr_genes %in% rownames(sub_allcells_css@assays$SCT@data)]

# calculate mean per cell type and condition for each LR gene
mean_exp_cond_lr = apply(sub_allcells_css@assays$SCT@data[all_lr_genes,], 1, 
                         function(x) tapply(x, paste0(sub_allcells_css$subpops, 
                                                      "_", sub_allcells_css$Condition), mean))

# determine the cell type and condition with the highest expression
max_cond_ct = rownames(mean_exp_cond_lr)[apply(mean_exp_cond_lr, 2, which.max)]
names(max_cond_ct) = colnames(mean_exp_cond_lr)
max_cond_ct_ct = unlist(lapply(strsplit(max_cond_ct, "_"), function(x) x[1]))
max_cond_ct_cond = unlist(lapply(strsplit(max_cond_ct, "_"), function(x) x[length(x)]))

# correlation of mean expression
cor_cond_lr = cor(mean_exp_cond_lr, method = "sp")

# filter correlation with itself, keep only genes with cor>=0.3
diag(cor_cond_lr) = 0
adj_cond_mat = cor_cond_lr>=0.3
```

Plot ligands and receptors with MDS

```{r}
# build graph, project with MDS
network_cond = graph_from_adjacency_matrix(adj_cond_mat, weighted=T, mode="undirected", diag=F)
l_cond = igraph::layout_with_mds(network_cond)
l_cond = data.frame(l_cond)
l_cond$gene = colnames(adj_cond_mat)
rownames(l_cond) = colnames(adj_cond_mat)

# define all edges, based on CellPhoneDB pairings
tmp_df = merge(gene_pairs_cond, l_cond, by.x = "gn1", by.y = "gene")
edge_cond_df = merge(tmp_df, l_cond, by.x = "gn2", by.y = "gene")
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_ct), by.x = 1, by.y = 0, all.x = T)
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_ct), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_df)[9:10] = c("ct_g1", "ct_g2")
# add highest expressing major cell types
edge_cond_df$maj_g1 = ifelse(grepl("LSEC", edge_cond_df$ct_g1), "Endothelial",
                      ifelse(grepl("Hepatocytes", edge_cond_df$ct_g1), "Hepatocytes",
                             ifelse(grepl("Stellate cells", edge_cond_df$ct_g1) |
                                    grepl("Fibroblast", edge_cond_df$ct_g1) |
                                    grepl("VSMC", edge_cond_df$ct_g1), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", edge_cond_df$ct_g1), "Cholangiocytes", 
                                           "Immune"))))
edge_cond_df$maj_g2 = ifelse(grepl("LSEC", edge_cond_df$ct_g2), "Endothelial",
                      ifelse(grepl("Hepatocytes", edge_cond_df$ct_g2), "Hepatocytes",
                             ifelse(grepl("Stellate cells", edge_cond_df$ct_g2) |
                                    grepl("Fibroblast", edge_cond_df$ct_g2) |
                                    grepl("VSMC", edge_cond_df$ct_g2), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", edge_cond_df$ct_g2), "Cholangiocytes", 
                                           "Immune"))))
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_cond), by.x = 1, by.y = 0, all.x = T)
edge_cond_df = merge(edge_cond_df, data.frame(max_cond_ct_cond), by.x = 2, by.y = 0, all.x = T)

# define the vertices of the network
point_cond_df = l_cond
point_cond_df$ct = max_cond_ct_ct[point_cond_df$gene]
point_cond_df$ct2 = ifelse(grepl("LSEC", point_cond_df$ct), "Endothelial",
                      ifelse(grepl("Hepatocytes", point_cond_df$ct), "Hepatocytes",
                             ifelse(grepl("Stellate cells", point_cond_df$ct) |
                                    grepl("VSMC", point_cond_df$ct) |
                                    grepl("Fibroblast", point_cond_df$ct), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", point_cond_df$ct), "Cholangiocytes", 
                                           "Immune"))))
point_cond_df$cond = max_cond_ct_cond[point_cond_df$gene]

# define the median points for each cell type (using max expression)
pe_l = makeMedian(point_cond_df, edge_cond_df, cl = c("ct", "ct_g1", "ct_g2"))

# plot total gene correlation projection and median network
pltboth = ggplot()+
  geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.25, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_segment(data = pe_l[[2]], 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq), 
               alpha = 0.15, show.legend = F)+
  geom_point(data = pe_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

pltboth = ggplot()+
  geom_segment(data = edge_cond_df, 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y), 
               alpha = 0.03, show.legend = F)+
  geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.6, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_point(data = pe_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

# get median per cell type, per condition - FULL NETWORK
pe_cond_l = makeMedianCond(point_cond_df, edge_cond_df, cl = c("ct", "ct_g1", "ct_g2"))

plt_cond_l = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
  plt_cond_l[[cc]] = ggplot()+
    geom_segment(data = pe_cond_l[[2]][pe_cond_l[[2]]$condition==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, 
                               size = Freq, alpha = Freq))+
    geom_point(data = pe_cond_l[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_cond_l[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_cond_l[["leg"]] = cowplot::get_legend(plt_cond_l[["healthy"]])

# plot FULL NETWORK median per condition
cowplot::plot_grid(plt_cond_l[[1]]+theme(legend.position = "none"), 
                   plt_cond_l[[2]]+theme(legend.position = "none"),
                   plt_cond_l[[3]]+theme(legend.position = "none"), plt_cond_l$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))

# get median per cell type, per condition - UNIQUE PER CONDTION NETWORK
pe_cond_l_u = makeMedianCond(point_cond_df, edge_cond_df[edge_cond_df$id_cp_interaction %in% unique_inters,],
                             cl = c("ct", "ct_g1", "ct_g2"))

plt_cond_l_u = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
  plt_cond_l_u[[cc]] = ggplot()+
    geom_segment(data = pe_cond_l_u[[2]][pe_cond_l_u[[2]]$condition==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_cond_l_u[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_u[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_cond_l_u[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_cond_l_u[["leg"]] = cowplot::get_legend(plt_cond_l_u[["healthy"]])

# plot UNIQUE PER CONDTION NETWORK median per condition
cowplot::plot_grid(plt_cond_l_u[[1]]+theme(legend.position = "none"), 
                   plt_cond_l_u[[2]]+theme(legend.position = "none"),
                   plt_cond_l_u[[3]]+theme(legend.position = "none"), plt_cond_l_u$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))

# get median per cell type, per condition - HEALTHY NETWORK
pe_cond_l_h = makeMedianCond(point_cond_df, edge_cond_df[edge_cond_df$id_cp_interaction %in% inter_df$healthy$id_cp_interaction,], cl = c("ct", "ct_g1", "ct_g2"))

plt_cond_l_h = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
  plt_cond_l_h[[cc]] = ggplot()+
    geom_segment(data = pe_cond_l_h[[2]][pe_cond_l_h[[2]]$condition==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_cond_l_h[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_h[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_cond_l_h[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_cond_l_h[["leg"]] = cowplot::get_legend(plt_cond_l_h[["healthy"]])

# plot HEALTHY NETWORK median per condition
cowplot::plot_grid(plt_cond_l_h[[1]]+theme(legend.position = "none"), 
                   plt_cond_l_h[[2]]+theme(legend.position = "none"),
                   plt_cond_l_h[[3]]+theme(legend.position = "none"), plt_cond_l_h$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))

# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_cond_l_ch = makeMedianCond(point_cond_df, 
                             edge_cond_df[edge_cond_df$id_cp_interaction %in% comph_inters,], 
                             cl = c("ct", "ct_g1", "ct_g2"))

plt_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
  plt_cond_l_ch[[cc]] = ggplot()+
    geom_segment(data = pe_cond_l_ch[[2]][pe_cond_l_ch[[2]]$condition==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_cond_l_ch[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_ch[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_cond_l_ch[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_cond_l_ch[["leg"]] = cowplot::get_legend(plt_cond_l_ch[["healthy"]])

# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_cond_l_ch[[1]]+theme(legend.position = "none"), 
                   plt_cond_l_ch[[2]]+theme(legend.position = "none"),
                   plt_cond_l_ch[[3]]+theme(legend.position = "none"), plt_cond_l_ch$leg, 
                   ncol = 2, rel_widths = c(1,1,1,0.5))
```

MDS with simplified labels

```{r}
# add labels to points
point_cond_df$ct_mid = point_cond_df$ct
point_cond_df$ct_mid[grepl("NK", point_cond_df$ct)] = "ILC"
point_cond_df$ct_mid[grepl("ILC", point_cond_df$ct)] = "ILC"
point_cond_df$ct_mid[grepl("T cells", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("MAIT", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("Treg", point_cond_df$ct)] = "T cells"
point_cond_df$ct_mid[grepl("Stellate", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("Fibroblast", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("VSMC", point_cond_df$ct)] = "Mesenchymal"
point_cond_df$ct_mid[grepl("LSEC", point_cond_df$ct)] = "LSEC"
point_cond_df$ct_mid[grepl("non-LSEC", point_cond_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Lymphatic EC", point_cond_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Dividing endothelial cells", point_cond_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_df$ct_mid[grepl("Monocytes", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("Macrophages", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("cDC", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("activated DCs", point_cond_df$ct)] = "other Mono-Mac"
point_cond_df$ct_mid[grepl("Kupffer", point_cond_df$ct)] = "Kupffer cells"
point_cond_df$ct_mid[grepl("Plasma", point_cond_df$ct)] = "B cells"

# add labels to edges
match_df = unique(data.frame("ct" = point_cond_df$ct,
                             "ct_mid" = point_cond_df$ct_mid))
rownames(match_df) = match_df$ct

edge_cond_df$mid_g1 = match_df[edge_cond_df$ct_g1,"ct_mid"]
edge_cond_df$mid_g2 = match_df[edge_cond_df$ct_g2,"ct_mid"]

pe_mid_l = makeMedian(point_cond_df, edge_cond_df, 
                      cl = c("ct_mid", "mid_g1", "mid_g2"))

# plot total gene correlation projection and median network
pltboth = ggplot()+
  geom_point(data = point_cond_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.25, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_segment(data = pe_mid_l[[2]], 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq), 
               alpha = 0.15, show.legend = F)+
  geom_point(data = pe_mid_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct_mid), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_cond_l_mid = makeMedianCond(point_cond_df, 
                                   edge_cond_df[edge_cond_df$id_cp_interaction%in%comph_inters,],
                                   edge_by = "condition", 
                               cl = c("ct_mid", "mid_g1", "mid_g2"))

plt_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$condition)){
  plt_cond_l_ch[[cc]] = ggplot()+
    geom_segment(data = pe_cond_l_mid[[2]][pe_cond_l_mid[[2]]$condition==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_cond_l_mid[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct_mid), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_cond_l_mid[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_cond_l_mid[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_cond_l_ch[["leg"]] = cowplot::get_legend(plt_cond_l_ch[["healthy"]])

# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_cond_l_ch[[1]]+theme(legend.position = "none"), 
                   plt_cond_l_ch[[2]]+theme(legend.position = "none"),
                   plt_cond_l_ch[[3]]+theme(legend.position = "none"), plt_cond_l_ch$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))
```

Save network objects

```{r}
saveRDS(comph_inters, file = "results/cell_comm/v3/comph_inters.RDS")
save(edge_cond_df, point_cond_df, file = "results/cell_comm/v3/networks_cond.RData")
save(pe_l, pe_cond_l, pe_cond_l_u, pe_cond_l_h, pe_cond_l_ch, 
     file = "results/cell_comm/v3/median_networks_cond.RData")
save(pe_mid_l, pe_cond_l_mid, 
     file = "results/cell_comm/v3/median_networks_cond_midct.RData")
```

Plot ligands and receptors with UMAP

```{r}
set.seed(2954)
l = uwot::umap(t(mean_exp_cond_lr), metric = "cosine", ret_nn = T, n_epochs = 1000)
l_cond = data.frame(l$embedding)
l_cond$gene = colnames(mean_exp_cond_lr)
rownames(l_cond) = colnames(mean_exp_cond_lr)
tmp_df = merge(gene_pairs_cond, l_cond, by.x = "gn1", by.y = "gene")
edge_cond_umap_df = merge(tmp_df, l_cond, by.x = "gn2", by.y = "gene")
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_ct), by.x = 1, by.y = 0, all.x = T)
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_ct), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_umap_df)[9:10] = c("ct_g1", "ct_g2")
edge_cond_umap_df$maj_g1 = ifelse(grepl("LSEC", edge_cond_umap_df$ct_g1), "Endothelial",
                      ifelse(grepl("Hepatocytes", edge_cond_umap_df$ct_g1), "Hepatocytes",
                             ifelse(grepl("Stellate cells", edge_cond_umap_df$ct_g1) |
                                    grepl("VSMC", edge_cond_umap_df$ct_g1) |
                                    grepl("Fibroblast", edge_cond_umap_df$ct_g1), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", edge_cond_umap_df$ct_g1),
                                           "Cholangiocytes", "Immune"))))
edge_cond_umap_df$maj_g2 = ifelse(grepl("LSEC", edge_cond_umap_df$ct_g2), "Endothelial",
                      ifelse(grepl("Hepatocytes", edge_cond_umap_df$ct_g2), "Hepatocytes",
                             ifelse(grepl("Stellate cells", edge_cond_umap_df$ct_g2) |
                                    grepl("VSMC", edge_cond_umap_df$ct_g2) |
                                    grepl("Fibroblast", edge_cond_umap_df$ct_g2), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", edge_cond_umap_df$ct_g2),
                                           "Cholangiocytes", "Immune"))))
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_cond), by.x = 1, by.y = 0, all.x = T)
edge_cond_umap_df = merge(edge_cond_umap_df, data.frame(max_cond_ct_cond), by.x = 2, by.y = 0, all.x = T)
colnames(edge_cond_umap_df)[4] = "cond"

point_cond_umap_df = l_cond
point_cond_umap_df$ct = max_cond_ct_ct[point_cond_umap_df$gene]
point_cond_umap_df$ct2 = ifelse(grepl("LSEC", point_cond_umap_df$ct), "Endothelial",
                      ifelse(grepl("Hepatocytes", point_cond_umap_df$ct), "Hepatocytes",
                             ifelse(grepl("Stellate cells", point_cond_umap_df$ct) |
                                    grepl("VSMC", point_cond_umap_df$ct) |
                                    grepl("Fibroblast", point_cond_umap_df$ct), "Mesenchymal",
                                    ifelse(grepl("Cholangiocytes", point_cond_umap_df$ct), "Cholangiocytes", 
                                           "Immune"))))
point_cond_umap_df$cond = max_cond_ct_cond[point_cond_umap_df$gene]


pe_umap_l = makeMedian(point_cond_umap_df, edge_cond_umap_df, cl = c("ct", "ct_g1", "ct_g2"))

# plot total gene correlation projection and median network
pltboth = ggplot()+
  geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.25, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_segment(data = pe_umap_l[[2]], 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq), 
               alpha = 0.15, show.legend = F)+
  geom_point(data = pe_umap_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

pltboth = ggplot()+
  geom_segment(data = edge_cond_umap_df, 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y), 
               alpha = 0.03, show.legend = F)+
  geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.6, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_point(data = pe_umap_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4), limits = range(pe_l[[2]]$Freq))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_umap_cond_l_ch = makeMedianCond(point_cond_umap_df, 
                                   edge_cond_umap_df[edge_cond_umap_df$id_cp_interaction%in%comph_inters,],
                                   edge_by = "cond", cl = c("ct", "ct_g1", "ct_g2"))

plt_umap_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$cond)){
  plt_umap_cond_l_ch[[cc]] = ggplot()+
    geom_segment(data = pe_umap_cond_l_ch[[2]][pe_umap_cond_l_ch[[2]]$cond==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_umap_cond_l_ch[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_umap_cond_l_ch[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_umap_cond_l_ch[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_umap_cond_l_ch[["leg"]] = cowplot::get_legend(plt_umap_cond_l_ch[["healthy"]])

# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_umap_cond_l_ch[[1]]+theme(legend.position = "none"), 
                   plt_umap_cond_l_ch[[2]]+theme(legend.position = "none"),
                   plt_umap_cond_l_ch[[3]]+theme(legend.position = "none"), plt_umap_cond_l_ch$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))
```

UMAPs with simplified labels

```{r}
# add labels to points
point_cond_umap_df$ct_mid = point_cond_umap_df$ct
point_cond_umap_df$ct_mid[grepl("NK", point_cond_umap_df$ct)] = "ILC"
point_cond_umap_df$ct_mid[grepl("ILC", point_cond_umap_df$ct)] = "ILC"
point_cond_umap_df$ct_mid[grepl("T cells", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("MAIT", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("Treg", point_cond_umap_df$ct)] = "T cells"
point_cond_umap_df$ct_mid[grepl("Stellate", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("VSMC", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("Fibroblast", point_cond_umap_df$ct)] = "Mesenchymal"
point_cond_umap_df$ct_mid[grepl("LSEC", point_cond_umap_df$ct)] = "LSEC"
point_cond_umap_df$ct_mid[grepl("non-LSEC", point_cond_umap_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Lymphatic EC", point_cond_umap_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Dividing endothelial cells", point_cond_umap_df$ct, 
                                fixed = T)] = "other ECs"
point_cond_umap_df$ct_mid[grepl("Monocytes", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("Macrophages", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("cDC", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("activated DCs", point_cond_umap_df$ct)] = "other Mono-Mac"
point_cond_umap_df$ct_mid[grepl("Kupffer", point_cond_umap_df$ct)] = "Kupffer cells"
point_cond_umap_df$ct_mid[grepl("Plasma", point_cond_umap_df$ct)] = "B cells"

# add labels to edges
match_df = unique(data.frame("ct" = point_cond_umap_df$ct,
                             "ct_mid" = point_cond_umap_df$ct_mid))
rownames(match_df) = match_df$ct

edge_cond_umap_df$mid_g1 = match_df[edge_cond_umap_df$ct_g1,"ct_mid"]
edge_cond_umap_df$mid_g2 = match_df[edge_cond_umap_df$ct_g2,"ct_mid"]

pe_umap_mid_l = makeMedian(point_cond_umap_df, edge_cond_umap_df, 
                           cl = c("ct_mid", "mid_g1", "mid_g2"))

# plot total gene correlation projection and median network
pltboth = ggplot()+
  geom_point(data = point_cond_umap_df, mapping = aes(x = X1, y = X2, colour = ct2), 
             alpha = 0.25, show.legend = F)+
  scale_shape_manual(values = c(0,4,19))+
  geom_segment(data = pe_umap_mid_l[[2]], 
               mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq), 
               alpha = 0.15, show.legend = F)+
  geom_point(data = pe_umap_mid_l[[1]], 
             mapping = aes(x = X1, y = X2, fill = ct_mid), 
             alpha = 1, pch = 21, size = 4)+
  scale_size_continuous(range = c(0, 4))+
  theme_classic()+ theme(aspect.ratio = 1)
print(pltboth)

# get median per cell type, per condition - HEALTHY COMPARISON NETWORK
pe_umap_cond_l_mid = makeMedianCond(point_cond_umap_df, 
                                   edge_cond_umap_df[edge_cond_umap_df$id_cp_interaction%in%comph_inters,],
                                   edge_by = "cond", cl = c("ct_mid", "mid_g1", "mid_g2"))

plt_umap_cond_l_ch = list()
for(cc in unique(pe_cond_l[[2]]$cond)){
  plt_umap_cond_l_ch[[cc]] = ggplot()+
    geom_segment(data = pe_umap_cond_l_mid[[2]][pe_umap_cond_l_mid[[2]]$cond==cc,], 
                 mapping = aes(x = X1.x, xend = X1.y, y = X2.x, yend = X2.y, size = Freq, alpha = Freq))+
    geom_point(data = pe_umap_cond_l_mid[[1]], 
               mapping = aes(x = X1, y = X2, fill = ct_mid), 
               alpha = 1, pch = 21, size = 4)+
    scale_size_continuous(range = c(0, 4), limits = range(pe_umap_cond_l_mid[[2]]$Freq))+
    scale_alpha_continuous(limits = range(pe_umap_cond_l_mid[[2]]$Freq))+
    labs(title = cc)+
    guides(size = guide_legend(direction = "horizontal", nrow = 2),
           alpha = guide_legend(direction = "horizontal", nrow = 2))+
    theme_classic()
}
plt_umap_cond_l_ch[["leg"]] = cowplot::get_legend(plt_umap_cond_l_ch[["healthy"]])

# plot HEALTHY COMPARISON NETWORK median per condition
cowplot::plot_grid(plt_umap_cond_l_ch[[1]]+theme(legend.position = "none"), 
                   plt_umap_cond_l_ch[[2]]+theme(legend.position = "none"),
                   plt_umap_cond_l_ch[[3]]+theme(legend.position = "none"), plt_umap_cond_l_ch$leg, 
                   ncol = 4, rel_widths = c(1,1,1,0.5))
```

Save UMAP network objects

```{r}
save(edge_cond_umap_df, point_cond_umap_df, 
     file = "results/cell_comm/v3/networks_cond_umap.RData")
save(pe_umap_l, pe_umap_cond_l_ch, 
     file = "results/cell_comm/v3/median_networks_cond_umap.RData")
save(pe_umap_mid_l, pe_umap_cond_l_mid, 
     file = "results/cell_comm/v3/median_networks_cond_umap_midct.RData")
```



